U.S. patent application number 17/333603 was filed with the patent office on 2021-11-25 for edge device with self-configuring sensor kit network for monitoring and managing industrial settings.
This patent application is currently assigned to STRONG FORCE IOT PORTFOLIO 2016, LLC. The applicant listed for this patent is STRONG FORCE IOT PORTFOLIO 2016, LLC. Invention is credited to Charles Cella, Gerald William Duffy, JR., Teymour El-Tahry, Jeffrey P. McGuckin, Richard Spitz.
Application Number | 20210365012 17/333603 |
Document ID | / |
Family ID | 1000005813614 |
Filed Date | 2021-11-25 |
United States Patent
Application |
20210365012 |
Kind Code |
A1 |
Cella; Charles ; et
al. |
November 25, 2021 |
EDGE DEVICE WITH SELF-CONFIGURING SENSOR KIT NETWORK FOR MONITORING
AND MANAGING INDUSTRIAL SETTINGS
Abstract
A variety of kits are provided that are configured with
components, systems and methods for monitoring various industrial
settings, including kits with self-configuring sensor networks,
communication gateways, and automatically configured back end
systems.
Inventors: |
Cella; Charles; (PEMBROKE,
MA) ; El-Tahry; Teymour; (DETROIT, MI) ;
Spitz; Richard; (FORT LAUDERDALE, FL) ; McGuckin;
Jeffrey P.; (PHILADELPHIA, PA) ; Duffy, JR.; Gerald
William; (PHILADELPHIA, PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
STRONG FORCE IOT PORTFOLIO 2016, LLC |
FORT LAUDERDALE |
FL |
US |
|
|
Assignee: |
STRONG FORCE IOT PORTFOLIO 2016,
LLC
Fort Lauderdale
FL
|
Family ID: |
1000005813614 |
Appl. No.: |
17/333603 |
Filed: |
May 28, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/US2019/059088 |
Oct 31, 2019 |
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17333603 |
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62914998 |
Oct 14, 2019 |
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62869011 |
Jun 30, 2019 |
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62827166 |
Mar 31, 2019 |
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62791878 |
Jan 13, 2019 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05B 19/4185 20130101;
H04L 67/34 20130101; G05B 19/4183 20130101; H04L 67/12 20130101;
H04L 41/16 20130101 |
International
Class: |
G05B 19/418 20060101
G05B019/418; H04L 29/08 20060101 H04L029/08; H04L 12/24 20060101
H04L012/24 |
Claims
1. A system comprising: a backend system; and a sensor kit
configured to monitor an industrial setting, the sensor kit
comprising: an edge device; and a plurality of sensors that capture
sensor data and transmit the sensor data via a self-configuring
sensor kit network, wherein the plurality of sensors includes one
or more sensors of a first sensor type and one or more sensors of a
second sensor type, wherein at least one sensor of the plurality of
sensors comprises: a sensing component that captures sensor
measurements and outputs instances of sensor data; a processing
unit that generates reporting packets based on one or more
instances of sensor data and outputs the reporting packets, wherein
each reporting packet includes routing data and one or more
instances of sensor data; and a communication device configured to
receive reporting packets from the processing unit and to transmit
the reporting packets to the edge device via the self-configuring
sensor kit network in accordance with a first communication
protocol; wherein the edge device comprises: a communication system
having: a first communication device that receives reporting
packets from the plurality of sensors via the self-configuring
sensor kit network; and a second communication device that
transmits sensor kit packets to a backend system via a public
network; and a processing system having one or more processors that
execute computer- executable instructions that cause the processing
system to: receive the reporting packets from the communication
system; perform one or more edge operations on the instances of
sensor data in the reporting packets; generate the sensor kit
packets based on the instances of sensor data, wherein each sensor
kit packet includes at least one instance of sensor data; and
output the sensor kits packets to the communication system, wherein
the communication system transmits the sensor kit packets to the
backend system via the public network; wherein the backend system
comprises: a backend storage system that stores a sensor kit data
store that stores sensor data received from one or more respective
sensor kits, including the sensor kit; and a backend processing
system having one or more processors that execute
computer-executable instructions that cause the backend processing
system to: receive the sensor kit packets from the sensor kit;
determine the sensor data collected by the sensor kit based on the
sensor kit packets; perform one or more backend operations on the
sensor data collected by the sensor kit; and store the sensor data
collected by the sensor kit in the sensor kit data store.
2. The system of claim 1, wherein the sensor kit further comprises
a gateway device, wherein the gateway device is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge
device.
3. The system of claim 2, wherein the gateway device includes a
satellite terminal device that is configured to transmit the sensor
kit packets to a satellite that routes the sensor kits to the
public network.
4. The system of claim 2, wherein the gateway device includes a
cellular chipset that is pre-configured to transmit sensor kit
packets to a cellphone tower of a preselected cellular
provider.
5. The system of claim 1, wherein the second communication device
of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network.
6. The system of claim 1, wherein the edge device further comprises
one or more storage devices that store a sensor data store that
stores instances of sensor data captured by the plurality of
sensors of the sensor kit.
7. The system of claim 1, wherein the edge device further comprises
one or more storage devices that store a model data store that
stores one or more machine-learned models that are each trained to
predict or classify a condition of an industrial component of the
industrial setting and/or the industrial setting based on a set of
features that are derived from instances of sensor data captured by
one or more of the plurality of sensors.
8. The system of claim 7, wherein performing one or more edge
operations includes: generating a feature vector based on one or
more instances of sensor data received from one or more sensors of
the plurality of sensors; inputting the feature vector to the
machine-learned model to obtain a prediction or classification
relating to a condition of a particular industrial component of the
industrial setting or the industrial setting and a degree of
confidence corresponding to the prediction or classification; and
selectively encoding the one or more instances of sensor data prior
to transmission to the backend system based on the condition or
prediction.
9. The system of claim 8, wherein selectively encoding the one or
more instances of sensor data includes: in response to obtaining
one or more predictions or classifications relating to conditions
of respective industrial components of the industrial setting and
the industrial setting that collectively indicate that there are
likely no issues relating to any industrial component of the
industrial setting and the industrial setting, compressing the one
or more instances of sensor data using a lossy codec.
10. The system of claim 9, wherein compressing the one or more
instances of sensor data using the lossy codec includes:
normalizing the one or more instances of sensor data into
respective pixel values; encoding the respective pixel values into
a media content frame; and compressing a block of media content
frames using the lossy codec to obtain a compressed block, wherein
the lossy codec is a video codec and the compressed block includes
the media content frame and one or more other media content frames
that include normalized pixel values of other instances of sensor
data.
11. The system of claim 10, wherein the backend system receives the
compressed block in one or more sensor kit packets and determines
the sensor data collected by the sensor kit by decompressing the
compressed block using the lossy codec.
12. The system of claim 8, wherein selectively encoding the one or
more instances of sensor data includes: in response to obtaining a
prediction or classification relating to a condition of a
particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting, compressing the one
or more instances of sensor data using a lossless codec.
13. The system of claim 8, wherein selectively encoding the one or
more instances of sensor data includes: in response to obtaining a
prediction or classification relating to a condition of a
particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting, refraining from
compressing the one or more instances of sensor data.
14. The system of claim 8, wherein selectively encoding the one or
more instances of sensor data includes selecting a stream of sensor
data instances for uncompressed transmission.
15. The system of claim 7, wherein performing one or more edge
operations includes: generating a feature vector based on one or
more instances of sensor data received from one or more sensors of
the plurality of sensors; inputting the feature vector to the
machine-learned model to obtain a prediction or classification
relating to a condition of a particular industrial component of the
industrial setting or the industrial setting and a degree of
confidence corresponding to the prediction or classification; and
selectively storing the one or more instances of sensor data in a
storage device of the edge device based on the prediction or
classification.
16. The system of claim 15, wherein selectively storing the one or
more instances of sensor data includes: in response to obtaining
one or more predictions or classifications relating to conditions
of respective industrial components of the industrial setting and
the industrial setting that collectively indicate that there are
likely no issues relating to any industrial component of the
industrial setting and the industrial setting, storing the one or
more instances of sensor data in the storage device with an expiry,
such that the one or more instances of sensor data are purged from
the storage device in accordance with the expiry.
17. The system of claim 15, wherein selectively storing the one or
more instances of sensor data includes: in response to obtaining a
prediction or classification relating to a condition of a
particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting, storing the one or
more instances of sensor data in the storage device
indefinitely.
18. The system of claim 1, wherein the self-configuring sensor kit
network is a star network such that each sensor of the plurality of
sensors transmits respective instances of sensor data with the edge
device directly using a short-range communication protocol.
19. The system of claim 18, wherein the computer-executable
instructions further cause the one or more processors of the edge
device to initiate configuration of the self-configuring sensor kit
network.
20. The system of claim 1, wherein the self-configuring sensor kit
network is a mesh network such that: the communication device of
each sensor of the plurality of sensors is configured to establish
a communication channel with at least one other sensor of the
plurality of sensors; at least one sensor of the plurality of
sensors is configured to receive instances of sensor data from one
or more other sensors of the plurality of sensors and to route the
received instances of the sensor data towards the edge device.
21. The system of claim 20, wherein the computer-executable
instructions further cause the one or more processors of the edge
device to initiate configuration of the self-configuring sensor kit
network, wherein the plurality of sensors form the mesh network in
response to the edge device initiating configuration of the
self-configuring sensor kit network.
22. The system of claim 1, wherein the self-configuring sensor kit
network is a hierarchical network.
23. The system of claim 22, wherein the sensor kit further
comprises one or more collection devices configured to receive
reporting packets from one or more sensors of the plurality of
sensors and route the reporting packets to the edge device.
24. The system of claim 1, wherein the backend operations include
performing one or more analytics tasks using the sensor data.
25. The system of claim 1, wherein the backend operations include
performing one or more artificial intelligence tasks using the
sensor data.
26. The system of claim 1, wherein the backend operations include
issuing a notification to a human user associated with the
industrial setting based on the sensor data.
27. The system of claim 1, wherein the backend operations include
controlling at least one component of the industrial setting based
on the sensor data.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a bypass continuation of International
Application No. PCT/US2019/059088, filed on Oct. 31, 2019, which
claims priority to U.S. Provisional Patent Application No.
62/791,878 filed on Jan. 13, 2019, U.S. Provisional Patent
Application No. 62/827,166 filed on Mar. 31, 2019, U.S. Provisional
Patent Application No. 62/869,011 filed on Jun. 30, 2019, and U.S.
Provisional Patent Application No. 62/914,998 filed on Oct. 14,
2019, each entitled METHODS, SYSTEMS, KITS, AND APPARATUSES FOR
MONITORING INDUSTRIAL SETTINGS. Each of the above-identified
applications is hereby incorporated by reference in its entirety as
if fully set forth herein.
FIELD
[0002] The present disclosure relates to various configurations of
Internet of Things (IoT) systems in conveniently deployed kits that
monitor or manage industrial settings using various configurations
of sensors, edge computing devices, networking systems, and
artificial intelligence.
BACKGROUND
[0003] The Internet of Things (IoT) is a network of connected
devices, systems, components, services, programs, vehicles,
appliances, machines, and other electronic items that communicate
via a set of communication networks and communication interfaces
and protocols. While much of the development in the IoT space has
centered on consumer products, such as wearable devices, home
monitoring systems, smart appliances, and the like, there are many
industrial applications for IoT devices and systems, including
embodiments described throughout this disclosure and in the
documents incorporated herein. For example, IoT sensors can be used
to monitor industrial facilities, such as factories, refineries,
oil and gas fields, manufacturing lines, energy production
facilities, mining environments, and the like, as well as the many
machines and systems disposed in such environments. While machines
may include embedded sensors and instrumentation, such as onboard
diagnostic systems, many machines do not have such embedded
sensors, and others only have a limited set of sensors;
accordingly, a need and an opportunity exist for vastly more data
collection, such as via the location (which may be temporary (such
as with portable or mobile data collectors as described in
documents incorporated by reference, or by drones, autonomous
vehicles, or the like), semi-permanent (such as with modular
interfaces for convenient connection and disconnection), or
permanent) of large numbers of heterogeneous sensors of various
types on, in or around machines in industrial environments.
[0004] There are a number of issues, however, that arise in the
Industrial IoT setting. For example, while many industrial IoT
devices may be configured to communicate using cellular protocols,
such as the 3G, 4G, LTE or 5G communication protocols, those
protocols may not be natively well suited for communication in the
industrial setting, as heavy machinery and thick dense structures
may adversely affect communication between devices. Wi-Fi systems
may also provide network connections within facilities; however,
Wi-Fi systems may also experience challenges due to the adverse
physical environments involved in industrial settings. For example,
Wi-Fi systems are not typically well designed to communicate
through obstructions, such as slabs of concrete or brick. Also,
many devices in an industrial setting may be mobile, such that
Wi-Fi and cellular systems have difficulty resolving which devices
are communicating at a given time.
[0005] Another issue that may arise is related to bandwidth. As
hundreds or thousands of sensors may be placed in an area to be
monitored (e.g., factory, assembly line, oil field, etc.), and
those sensors may capture multiple readings every second, the
amount of data that is being collected may put a strain on the
computing resources of even the most robust computing systems. A
need exists for methods and systems that address challenges of
efficient and effective bandwidth utilization.
[0006] Another issue is security. IoT devices can be perceived as
security risks when the devices are connected to computer networks,
such as ones used to operate mission critical machines. IoT devices
have historically experienced security vulnerabilities and have
frequently been points of attack on networks and devices.
[0007] Concerns about bandwidth, reliability, latency and/or
security may deter organizations from integrating IoT sensor
systems into their industrial environments and computer networks. A
need exists for systems that provide the benefits of the IoT while
addressing networking needs and security risks.
[0008] Another challenge for organizations considering IoT
deployments is that such deployments require sophisticated
integration of IoT devices with networking systems and with
platforms (e.g., cloud platforms) where analysis of IoT-collected
data is performed and where both human and automated controls are
provided for industrial settings. Organizations may lack the range
of expertise or available staff to undertake effective IoT
integrations. A need exists for simplified deployment systems that
offer the benefits of the IoT.
SUMMARY
[0009] Provided herein are methods and systems for monitoring and
managing industrial settings, including through a variety of
configurable kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring and managing
industrial settings while mitigating issues of complexity,
integration, bandwidth, latency and security. The practical
implementation of an IoT solution may include a set of components
that may comprise an appropriate set of sensors each configured for
various respective industrial settings, a set of communication
devices, a set of edge computing devices and a set of communication
capabilities (including various protocols, ports, gateways,
connectors, interfaces and the like) that collectively provide
automatically configured and/or pre-configured processing and
transmission of sensor data from the sensor kits to a set of
backend systems (e.g., cloud-deployed systems or on-premises
systems) via appropriate protocols, and a set of backend systems
that are automatically configured and/or preconfigured to provide
monitoring and/or management information to owners and operators of
industrial settings from the particular sensor kits that are
registered to their industrial settings. As used herein "set" may
include a set with a single member. References to "monitoring"
and/or to "management" should be understood, except where context
indicates otherwise, to encompass various actions or activities
that may benefit from the information shared via the IoT, such as
monitoring machine performance, reporting on status, states, or
conditions, managing states, conditions, parameters, undertaking
remote control, supporting autonomous functions that depend on
status or state information, supporting analytics, supporting
self-configuration, supporting artificial intelligence, supporting
machine learning, and the like.
[0010] According to some embodiments of the present disclosure, a
sensor kit configured for monitoring an industrial setting is
disclosed. In embodiments, the sensor kit includes an edge device
and a plurality of sensors, i.e., a set of sensors, that capture
sensor data and transmit the sensor data via a self-configuring
sensor kit network. The plurality of sensors includes one or more
sensors of a first sensor type and one or more sensors of a second
sensor type. At least one sensor of the plurality of sensors
includes a sensing component that captures sensor measurements and
outputs instances of sensor data; a processing unit that generates
reporting packets based on one or more instances of sensor data and
outputs the reporting packets, wherein each reporting packet
includes routing data and one or more instances of sensor data; and
a communication device configured to receive reporting packets from
the processing unit and to transmit the reporting packets to the
edge device via the self-configuring sensor kit network in
accordance with a first communication protocol. The edge device
includes a communication system having: a first communication
device that receives reporting packets from the plurality of
sensors via the self-configuring sensor kit network and a second
communication device that transmits sensor kit packets to a backend
system via a public network. The edge device further includes a
processing system having one or more processors that execute
computer-executable instructions that cause the processing system
to: receive the reporting packets from the communication system;
perform one or more edge operations on the instances of sensor data
in the reporting packets; generate the sensor kit packets based on
the instances of sensor data, wherein each sensor kit packet
includes at least one instance of sensor data; and output the
sensor kits packets to the communication system, wherein the
communication system transmits the reporting packets to the backend
system via the public network.
[0011] In some embodiments, the sensor kit further includes a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device. In some of these embodiments, the
gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. Alternatively, in
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0012] In some embodiments, the second communication device of the
edge device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network.
[0013] In some embodiments, the edge device further includes one or
more storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit.
[0014] In some embodiments, the edge device further includes one or
more storage devices that store a model data store that stores one
or more machine-learned models that are each trained to predict or
classify a condition of an industrial component of the industrial
setting and/or the industrial setting based on a set of features
that are derived from instances of sensor data captured by one or
more of the plurality of sensors. In some of these embodiments,
performing one or more edge operations includes: generating a
feature vector based on one or more instances of sensor data
received from one or more sensors of the plurality of sensors;
inputting the feature vector to the machine-learned model to obtain
a prediction or classification relating to a condition of a
particular industrial component of the industrial setting or the
industrial setting and a degree of confidence corresponding to the
prediction or classification; and selectively encoding the one or
more instances of sensor data prior to transmission to the backend
system based on the condition or prediction. In some of these
embodiments, selectively encoding the one or more instances of
sensor data includes: compressing the one or more instances of
sensor data using a lossy codec in response to obtaining one or
more predictions or classifications relating to conditions of
respective industrial components of the industrial setting and the
industrial setting that collectively indicate that there are likely
no issues relating to any industrial component of the industrial
setting and the industrial setting. In some of these embodiments,
compressing the one or more instances of sensor data using the
lossy codec includes: normalizing the one or more instances of
sensor data into respective pixel values; encoding the respective
pixel values into a video frame; and compressing a block of video
frames using the lossy codec, wherein the lossy codec is a video
codec and the block of video frames includes the video frame and
one or more other video frames that include normalized pixel values
of other instances of sensor data. In some embodiments, selectively
encoding the one or more instances of sensor data includes
compressing the one or more instances of sensor data using a
lossless codec in response to obtaining a prediction or
classification relating to a condition of a particular industrial
component or the industrial setting that indicates that there is
likely an issue relating to the particular industrial component or
the industrial setting. In some embodiments, selectively encoding
the one or more instances of sensor data includes refraining from
compressing the one or more instances of sensor data in response to
obtaining a prediction or classification relating to a condition of
a particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting. In some
embodiments, performing one or more edge operations includes:
generating a feature vector based on one or more instances of
sensor data received from one or more sensors of the plurality of
sensors; inputting the feature vector to the machine-learned model
to obtain a prediction or classification relating to a condition of
a particular industrial component of the industrial setting or the
industrial setting and a degree of confidence corresponding to the
prediction or classification; and selectively storing the one or
more instances of sensor data in a storage device of the edge
device based on the prediction or classification. In some
embodiments, selectively storing the one or more instances of
sensor data includes in response to obtaining one or more
predictions or classifications relating to conditions of respective
industrial components of the industrial setting and the industrial
setting that collectively indicate that there are likely no issues
relating to any industrial component of the industrial setting and
the industrial setting, storing the one or more instances of sensor
data in the storage device with an expiry, such that the one or
more instances of sensor data are purged from the storage device in
accordance with the expiry. In some embodiments, selectively
storing the one or more instances of sensor data includes in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the industrial setting,
storing the one or more instances of sensor data in the storage
device indefinitely.
[0015] In embodiments, the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In some of
these embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to initiate
configuration of the self-configuring sensor kit network.
[0016] In embodiments, the self-configuring sensor kit network is a
mesh network such that the communication device of each sensor of
the plurality of sensors is configured to establish a communication
channel with at least one other sensor of the plurality of sensors,
and at least one sensor of the plurality of sensors is configured
to receive instances of sensor data from one or more other sensors
of the plurality of sensors and to route the received instances of
the sensor data towards the edge device. In some of these
embodiments, the computer-executable instructions further cause the
one or more processors of the edge device to initiate configuration
of the self-configuring sensor kit network, wherein the plurality
of sensors form the mesh network in response to the edge device
initiating configuration of the self-configuring sensor kit
network.
[0017] In embodiments, the self-configuring sensor kit network is a
hierarchical network. In some of these embodiments, the sensor kit
further includes one or more collection devices configured to
receive reporting packets from one or more sensors of the plurality
of sensors and route the reporting packets to the edge device.
[0018] In embodiments, the self-configuring sensor kit network is a
ring network that communicates using a serial data protocol.
[0019] In embodiments, the sensor kit network is a mesh
network.
[0020] In embodiments, at least one of the sensors in the sensor
kit network is a multi-axis vibration sensor.
[0021] In embodiments, the edge device includes a rule-based
network protocol adaptor for selecting a network protocol by which
to send sensor kit packets via the public network.
[0022] According to some embodiments of the present disclosure, a
method for monitoring an industrial setting using a sensor kit
having a plurality of sensors and an edge device including a
processing system is disclosed. In embodiments, the method includes
receiving, by the processing system, reporting packets from one or
more respective sensors of the plurality of sensors, wherein each
reporting packet is sent from a respective sensor and indicates
sensor data captured by the respective sensor; performing, by the
processing system, one or more edge operations on one or more
instances of sensor data received in the reporting packets;
generating, by the processing system, one or more sensor kit
packets based on the instances of sensor data, wherein each sensor
kit packet includes at least one instance of sensor data; and
outputting, by the processing system, the sensor kit packets to a
backend system via a public network. In some embodiments, the
reporting packets received from one or more respective sensors of
the plurality of sensors include a sensor identifier of the
respective sensor. In embodiments, receiving the reporting packets
from the one or more respective sensors is performed using a first
communication device implementing a first communication protocol
and outputting the sensor kit packets to the backend system is
performed using a second communication device implementing a second
communication protocol.
[0023] In some embodiments, the second communication device is a
satellite terminal device, and outputting the sensor kit packets
includes transmitting the sensor kit packets to a satellite using
the satellite terminal device, wherein the satellite routes the
sensor kit packets to the public network. In embodiments,
outputting the sensor kit packets to a backend system includes
transmitting the sensor kit packets to a gateway device of the
sensor kit. In some embodiments, transmitting the sensor kit
packets to the gateway device includes transmitting the sensor kit
packets to the gateway via a wired communication link between the
edge device and the gateway device. In embodiments, the gateway
device includes a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network. In some embodiments, the gateway
device includes a cellular chipset that is pre-configured to
transmit sensor kit packets to a cellphone tower of a preselected
cellular provider. In embodiments, the method further includes
storing, by one or more storage devices of the edge device, a model
data store that stores one or more machine-learned models. In some
embodiments, the one or more machine-learned models are trained to
predict or classify a condition of an industrial component of the
industrial setting and/or of the industrial setting based on a set
of features that are derived from instances of sensor data captured
by one or more of the plurality of sensors.
[0024] In some embodiments performing one or more edge operations
includes generating a feature of vector based on one or more
instances of sensor data received from one or more sensors of the
plurality of sensors; inputting the feature vector to a
machine-learned model of the one or more machine-learned models to
obtain a prediction or classification relating to a condition of a
particular industrial component of the industrial setting or the
industrial setting and a degree of confidence corresponding to the
prediction or classification; and selectively encoding the one or
more instances of sensor data prior to transmission to the backend
system based on the condition or prediction. In some embodiments,
selectively encoding the one or more instances of sensor data
includes: compressing the one or more instances of sensor data
using a lossy codec in response to obtaining one or more
predictions or classifications relating to conditions of respective
industrial components of the industrial setting and the industrial
setting that collectively indicate that there are likely no issues
relating to any industrial component of the industrial setting and
the industrial setting. In embodiments, compressing the one or more
instances of sensor data using the lossy codec includes:
normalizing the one or more instances of sensor data into
respective pixel values; encoding the respective pixel values into
a video frame; and compressing a block of video frames using the
lossy codec, wherein the lossy codec is a video codec and the block
of video frames includes the video frame and one or more other
video frames that include normalized pixel values of other
instances of sensor data. In some embodiments, selectively encoding
the one or more instances of sensor data includes compressing the
one or more instances of sensor data using a lossless codec in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the industrial setting. In
embodiments, selectively encoding the one or more instances of
sensor data includes refraining from compressing the one or more
instances of sensor data in response to obtaining a prediction or
classification relating to a condition of a particular industrial
component or the industrial setting that indicates that there is
likely an issue relating to the particular industrial component or
the industrial setting.
[0025] In some embodiments, performing one or more edge operations
includes: generating a feature vector based on one or more
instances of sensor data received from one or more sensors of the
plurality of sensors; inputting the feature vector to the
machine-learned model to obtain a prediction or classification
relating to a condition of a particular industrial component of the
industrial setting or the industrial setting and a degree of
confidence corresponding to the prediction or classification; and
selectively storing the one or more instances of sensor data in a
storage device of the edge device based on the prediction or
classification. In embodiments, selectively storing the one or more
instances of sensor data includes storing the one or more instances
of sensor data in the storage device with an expiry such that the
one or more instances of sensor data are purged from the storage
device in accordance with the expiry, wherein storing the one or
more instances of sensor data in the storage device with an expiry
is performed in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial setting and the industrial setting
that collectively indicate that there are likely no issues relating
to any industrial component of the industrial setting and the
industrial setting. In some embodiments, selectively storing the
one or more instances of sensor data includes storing the one or
more instances of sensor data in the storage device indefinitely in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the industrial setting.
[0026] In some embodiments, the method further includes: capturing,
by a sensing component of a sensor of the plurality of sensors,
sensor measurements; generating, by a processor of the sensor, one
or more reporting packets based on the captured sensor
measurements; and transmitting, by a communication unit of the
sensor, the one or more reporting packets to the edge device via a
self-configuring sensor kit network. In some of these embodiments,
the method further includes initiating, by the processing system,
configuration of the self-configuring sensor kit network, wherein
the self-configuring sensor kit network is a star network. In some
embodiments, the reporting packets are received directly from
respective sensors using a short-range communication protocol. In
embodiments, the method further includes initiating, by the
processing system, configuration of the self-configuring sensor kit
network, wherein the self-configuring sensor kit network is a mesh
network. In some embodiments, the method further includes:
establishing, by the communication device of each sensor of the
plurality of sensors, a communication channel with at least one
other sensor of the plurality of sensors; receiving, by the at
least one sensor of the plurality of sensors, instances of sensor
data from one or more other sensors of the plurality of sensors;
and routing, by the at least one sensor of the plurality of
sensors, the received instances of the sensor data towards the edge
device via the mesh network.
[0027] In some embodiments, the self-configuring sensor kit network
is a hierarchical network and the sensor kit includes one or more
collection devices that participate in the hierarchical network. In
some of these embodiments, the method further includes receiving,
by a collection device of the one or more collection devices,
reporting packets from a set of sensors of the plurality of sensors
that communicate with the collection device using a first
short-range communication protocol; and routing, by the one or more
collection devices, the reporting packets to the edge device using
one of the first short-range communication protocol or a second
short-range communication protocol that is different than the
second-range communication protocol.
[0028] In some embodiments, the edge device includes a rule-based
network protocol adaptor. In some of these embodiments, the method
further includes: selecting, by the rule-based network protocol
adaptor, a network protocol; and sending, by the edge device,
sensor kit packets by the network protocol via the public
network.
[0029] In some embodiments, the plurality of sensors includes a
first set of sensors of a first sensor type and a second set of
sensors of a second sensor type.
[0030] According to some embodiments of the present disclosure, a
sensor kit configured for monitoring an industrial setting is
disclosed. In embodiments, the sensor kit includes an edge device
and a plurality of sensors that capture sensor data and transmit
the sensor data via a self-configuring sensor kit network. The
plurality of sensors includes one or more sensors of a first sensor
type and one or more sensors of a second sensor type. At least one
sensor of the plurality of sensors includes a sensing component
that captures sensor measurements and outputs instances of sensor
data; a processing unit that generates reporting packets based on
one or more instances of sensor data and outputs the reporting
packets, wherein each reporting packet includes routing data and
one or more instances of sensor data; and a communication device
configured to receive reporting packets from the processing unit
and to transmit the reporting packets to the edge device via the
self-configuring sensor kit network in accordance with a first
communication protocol. The edge device includes one or more
storage devices that store a model data store that stores a
plurality of machine-learned models that are each trained to
predict or classify a condition of an industrial component of the
industrial setting or of the industrial setting based on a set of
features that are derived from instances of sensor data captured by
one or more of the plurality of sensors. The edge device further
includes a communication system that receives reporting packets
from the plurality of sensors via the self-configuring sensor kit
network using a first communication protocol and that transmits
sensor kit packets to a backend system via a public network using a
second communication protocol that is different from the first
communication protocol. The edge device further includes a
processing system having one or more processors that execute
computer-executable instructions that cause the processing system
to: receive the reporting packets from the communication system;
generate a set of feature vectors based on one or more respective
instances of sensor data received in the reporting packets; for
each respective feature vector, input the respective feature vector
into a respective machine-learned model that corresponds to the
feature vector to obtain a respective prediction or classification
relating to a condition of a respective industrial component of the
industrial setting or the industrial setting and a degree of
confidence corresponding to the respective prediction or
classification; selectively encode the one or more instances of
sensor data prior to transmission to the backend system based on
the respective predictions or classifications outputted by the
machine-learned models in response to the respective feature vector
to obtain one or more sensor kit packets; and output the sensor
kits packets to the communication system, wherein the communication
system transmits the reporting packets to the backend system via
the public network.
[0031] In some embodiments, the sensor kit further includes a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device. In some of these embodiments, the
gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. Alternatively, in
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0032] In some embodiments, the second communication device of the
edge device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network.
[0033] In embodiments, the one or more storage devices that store a
sensor data store that stores instances of sensor data captured by
the plurality of sensors of the sensor kit.
[0034] In embodiments, selectively encoding the one or more
instances of sensor data includes, in response to obtaining one or
more predictions or classifications relating to conditions of
respective industrial components of the industrial setting and the
industrial setting that collectively indicate that there are likely
no issues relating to any industrial component of the industrial
setting and the industrial setting, compressing the one or more
instances of sensor data using a lossy codec. In some embodiments,
compressing the one or more instances of sensor data using the
lossy codec includes: normalizing the one or more instances of
sensor data into respective pixel values; encoding the respective
pixel values into a video frame; and compressing a block of video
frames using the lossy codec, wherein the lossy codec is a video
codec and the block of video frames includes the video frame and
one or more other video frames that include normalized pixel values
of other instances of sensor data. In some of these embodiments,
selectively encoding the one or more instances of sensor data
includes: in response to obtaining a prediction or classification
relating to a condition of a particular industrial component or the
industrial setting that indicates that there is likely an issue
relating to the particular industrial component or the industrial
setting, compressing the one or more instances of sensor data using
a lossless codec.
[0035] In some embodiments, selectively encoding the one or more
instances of sensor data includes: in response to obtaining a
prediction or classification relating to a condition of a
particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting, refraining from
compressing the one or more instances of sensor data.
[0036] In embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to selectively
store the one or more instances of sensor data in the one or more
storage devices of the edge device based on the respective
predictions or classifications. In some of these embodiments,
selectively storing the one or more instances of sensor data
includes, in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial setting and the industrial setting
that collectively indicate that there are likely no issues relating
to any industrial component of the industrial setting and the
industrial setting, storing the one or more instances of sensor
data in the storage device with an expiry, such that the one or
more instances of sensor data are purged from the storage device in
accordance with the expiry. In some embodiments, selectively
storing the one or more instances of sensor data includes, in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the industrial setting,
storing the one or more instances of sensor data in the storage
device indefinitely.
[0037] In embodiments, the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In some of
these embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to initiate
configuration of the self-configuring sensor kit network.
[0038] In some embodiments, the self-configuring sensor kit network
is a mesh network such that: the communication device of each
sensor of the plurality of sensors is configured to establish a
communication channel with at least one other sensor of the
plurality of sensors, and at least one sensor of the plurality of
sensors is configured to receive instances of sensor data from one
or more other sensors of the plurality of sensors and to route the
received instances of the sensor data towards the edge device. In
some of these embodiments, the computer-executable instructions
further cause the one or more processors of the edge device to
initiate configuration of the self-configuring sensor kit network,
wherein the plurality of sensors form the mesh network in response
to the edge device initiating configuration of the self-configuring
sensor kit network.
[0039] In embodiments, the self-configuring sensor kit network is a
hierarchical network. In some of these embodiments, the sensor kit
includes one or more collection devices configured to receive
reporting packets from one or more sensors of the plurality of
sensors and route the reporting packets to the edge device.
[0040] According to some embodiments of the present disclosure, a
method for monitoring an industrial setting using a sensor kit
having a plurality of sensors and an edge device including a
processing system is disclosed. The method includes: receiving, by
the processing system, reporting packets from one or more
respective sensors of the plurality of sensors, wherein each
reporting packet includes routing data and one or more instances of
sensor data; generating, by the processing system, a set of feature
vectors based on one or more respective instances of sensor data
received in the reporting packets; inputting, by the processing
system, each respective feature vector into a respective
machine-learned model of a plurality of machine-learned models that
are each trained to predict or classify a respective condition of
an industrial component of the industrial setting or of the
industrial setting based on a set of features that are derived from
instances of sensor data captured by one or more of the plurality
of sensors; obtaining, by the processing system, a respective
prediction or classification and a degree of confidence
corresponding to the respective prediction or classification from
each respective machine-learned model based on the respective
feature vector inputted into the respective machine-learned model;
selectively encoding, by the processing system, the one or more
instances of sensor data based on the respective prediction or
classification to obtain one or more sensor kit packets; and
transmitting, by the processing system, the sensor kit packets to a
backend system via a public network. In some embodiments, the
sensor kit includes a gateway device configured to receive sensor
kit packets from the edge device via a wired communication link and
transmit the sensor kit packets to the backend system via the
public network on behalf of the edge device. In embodiments, the
gateway device includes a satellite terminal device that transmits
the sensor kit packets to a satellite that routes the sensor kit
packets to the public network. In some embodiments, the gateway
device includes a cellular chipset that transmits the sensor kit
packets to a cellphone tower of a preselected cellular provider. In
embodiments, receiving the reporting packets from the one or more
respective sensors is performed using a first communication device
implementing a first communication protocol and transmitting the
sensor kit packets to the backend system is performed using a
second communication device implementing a second communication
protocol. In some embodiments, the second communication device of
the edge device is a satellite terminal device and transmitting the
sensor kit packets to the backend system includes transmitting, by
the satellite terminal device, the sensor kit packets to a
satellite that routes the sensor kit packets to the public
network
[0041] In some embodiments, the method further includes
compressing, by the processing system, the one or more instances of
sensor data using a lossy codec in response to obtaining one or
more predictions or classifications relating to conditions of the
respective industrial components of the industrial setting and the
industrial setting that collectively indicate that there are likely
no issues relating to any industrial component of the industrial
setting and the industrial setting. In some of these embodiments,
compressing the one or more instances of sensor data using the
lossy codec includes: normalizing the one or more instances of
sensor data into respective pixel values; encoding the respective
pixel values into a video frame; and compressing a block of video
frames using the lossy codec, wherein the lossy codec is a video
codec and the block of video frames includes the video frame and
one or more other video frames that include normalized pixel values
of other instances of the sensor data. In some embodiments, the
method includes compressing, by the processing system, the one or
more instances of sensor data using a lossless codec in response to
obtaining a prediction or classification relating to a condition of
a particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting. In embodiments, the
method includes refraining, by the processing system, from
compressing the one or more instances of sensor data in response to
obtaining a prediction or classification relating to a condition of
a particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting.
[0042] In some embodiments, the edge communication device includes
one or more storage devices that store the plurality of
machine-learned models. In some of these embodiments, the one or
more storage devices store instances of the sensor data captured by
the plurality of sensors of the sensor kit. In some embodiments,
the method further includes selectively storing, by the processing
system, the one or more instances of sensor data in the one or more
storage devices based on the respective predictions or
classifications. In embodiments, the method further includes
storing, by the processing system, the one or more instances of
sensor data in the storage device with an expiry such that the one
or more instances of sensor data are purged from the storage device
in accordance with the expiry, wherein the processing system stores
the one or more instances of sensor data in the storage device with
the expiry in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial setting and the industrial setting
that collectively indicate that there are likely no issues relating
to any industrial component of the industrial setting and the
industrial setting. In some embodiments, the method further
includes storing, by the processing system, the one or more
instances of sensor data in the storage device indefinitely in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the industrial setting
[0043] In some embodiments, the method further includes capturing,
by the plurality of sensors, sensor data; and transmitting, by the
plurality of sensors, the sensor data via a self-configuring sensor
kit network. In some of these embodiments, transmitting the sensor
data via the self-configuring sensor kit network includes directly
transmitting, by each sensor of the plurality of sensors, instances
of sensor data with the edge device using a short-range
communication protocol, wherein the self-configuring sensor kit
network is a star network. In some embodiments, the method further
includes initiating, by the processing system, configuration of the
self-configuring sensor kit network. In embodiments, the
self-configuring sensor kit network is a mesh network and each
sensor of the plurality of sensors includes a communication device.
In embodiments, the method further includes: establishing, by the
communication device of each sensor of the plurality of sensors, a
communication channel with at least one other sensor of the
plurality of sensors; receiving, by at least one sensor of the
plurality of sensors, instances of sensor data from one or more
other sensors of the plurality of sensors; and routing, by the at
least one sensor of the plurality of sensors, the received
instances of the sensor data towards the edge device.
[0044] In some embodiments, the self-configuring sensor kit network
is a hierarchical network and the sensor kit includes one or more
collection devices. In some of these embodiments, the method
further includes: receiving, by at least one collection device of
the plurality of collection devices, reporting packets from one or
more sensors of the plurality of sensors; and routing, by the at
least one collection device of the plurality of collection devices,
the reporting packets to the edge device.
[0045] In embodiments, the plurality of sensors includes a first
set of sensors of a first sensor type and a second set of sensors
of a second sensor type.
[0046] According to some embodiments of the present disclosure, a
sensor kit configured for monitoring an industrial setting is
disclosed. In embodiments, the sensor kit includes an edge device
and a plurality of sensors that capture sensor data and transmit
the sensor data via a self-configuring sensor kit network. The
plurality of sensors includes one or more sensors of a first sensor
type and one or more sensors of a second sensor type. At least one
sensor of the plurality of sensors includes a sensing component
that captures sensor measurements and outputs instances of sensor
data; a processing unit that generates reporting packets based on
one or more instances of sensor data and outputs the reporting
packets, wherein each reporting packet includes routing data and
one or more instances of sensor data; and a communication device
configured to receive reporting packets from the processing unit
and to transmit the reporting packets to the edge device via the
self-configuring sensor kit network in accordance with a first
communication protocol. The edge device includes a first
communication device that receives reporting packets from the
plurality of sensors via the self-configuring sensor kit network;
and a second communication device that transmits sensor kit packets
to a backend system via a public network. The edge device further
includes a processing system having one or more processors that
execute computer-executable instructions that cause the processing
system to: receive the reporting packets from the communication
system; generate a block of media content frames, wherein each
media content frame includes a plurality of frame values, each
frame value being indicative of a respective instance of sensor
data; compress the block of media content frames using a media
codec; generate one or more server kit packets based on the block
of media content frames; and transmit the one or more server kit
packets to the backend system via the public network.
[0047] In some embodiments, the sensor kit further includes a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device. In some of these embodiments, the
gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. Alternatively, in
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0048] In some embodiments, the second communication device of the
edge device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network.
[0049] In embodiments, the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit.
[0050] In embodiments, the edge device further includes one or more
storage devices that store a model data store that stores one or
more machine-learned models that are each trained to predict or
classify a condition of an industrial component of the industrial
setting and/or the industrial setting based on a set of features
that are derived from instances of sensor data captured by one or
more of the plurality of sensors. In some embodiments, performing
one or more edge operations includes: generating a feature vector
based on one or more instances of sensor data received from one or
more sensors of the plurality of sensors; inputting the feature
vector to the machine-learned model to obtain a prediction or
classification relating to a condition of a particular industrial
component of the industrial setting or the industrial setting and a
degree of confidence corresponding to the prediction or
classification; and selecting the codec used to compress the block
of media frames based on the condition or prediction. In some
embodiments, selecting the codec includes, in response to obtaining
one or more predictions or classifications relating to conditions
of respective industrial components of the industrial setting and
the industrial setting that collectively indicate that there are
likely no issues relating to any industrial component of the
industrial setting and the industrial setting, selecting a lossy
codec. In some of these embodiments, selectively encoding the one
or more instances of sensor data includes, in response to obtaining
a prediction or classification relating to a condition of a
particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting, selecting a
lossless codec.
[0051] In some embodiments, performing one or more edge operations
includes: generating a feature vector based on one or more
instances of sensor data received from one or more sensors of the
plurality of sensors; inputting the feature vector to the
machine-learned model to obtain a prediction or classification
relating to a condition of a particular industrial component of the
industrial setting or the industrial setting and a degree of
confidence corresponding to the prediction or classification; and
selectively storing the one or more instances of sensor data in a
storage device of the edge device based on the prediction or
classification. In some of these embodiments, selectively storing
the one or more instances of sensor data includes: in response to
obtaining one or more predictions or classifications relating to
conditions of respective industrial components of the industrial
setting and the industrial setting that collectively indicate that
there are likely no issues relating to any industrial component of
the industrial setting and the industrial setting, storing the one
or more instances of sensor data in the storage device with an
expiry, such that the one or more instances of sensor data are
purged from the storage device in accordance with the expiry. In
some embodiments, selectively storing the one or more instances of
sensor data includes: in response to obtaining a prediction or
classification relating to a condition of a particular industrial
component or the industrial setting that indicates that there is
likely an issue relating to the particular industrial component or
the industrial setting, storing the one or more instances of sensor
data in the storage device indefinitely.
[0052] In embodiments, the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In some of
these embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to initiate
configuration of the self-configuring sensor kit network.
[0053] In some embodiments, the self-configuring sensor kit network
is a mesh network such that: the communication device of each
sensor of the plurality of sensors is configured to establish a
communication channel with at least one other sensor of the
plurality of sensors, and at least one sensor of the plurality of
sensors is configured to receive instances of sensor data from one
or more other sensors of the plurality of sensors and to route the
received instances of the sensor data towards the edge device. In
some of these embodiments, the computer-executable instructions
further cause the one or more processors of the edge device to
initiate configuration of the self-configuring sensor kit network,
wherein the plurality of sensors form the mesh network in response
to the edge device initiating configuration of the self-configuring
sensor kit network.
[0054] In embodiments, the self-configuring sensor kit network is a
hierarchical network. In some of these embodiments, the sensor kit
includes one or more collection devices configured to receive
reporting packets from one or more sensors of the plurality of
sensors and route the reporting packets to the edge device.
[0055] In some embodiments, generating the block of media frames
includes: for each instance of sensor data that is to be included
in a media frame, normalizing the instance of sensor data into a
respective normalized media frame value that is within of range of
media frame values that are permitted by an encoding standard
corresponding to the media frame; and embedding each respective
normalized media frame value into the media frame. In some of these
embodiments, wherein each media frame is a video frame including a
plurality of pixels and the respective normalized media frame
values are pixel values. In some embodiments, embedding each
respective normalized media frame value into the media frame
includes: determining a pixel of the plurality of pixels
corresponding to the respective normalized media frame based on a
mapping that maps respective sensors of the plurality of sensors to
respective pixels of the plurality of pixels; and setting a value
of the determined pixel equal to the respective normalized media
frame value. In embodiments, the codec is an H.264/MPEG-4 codec. In
embodiments, the codec is an H.265/MPEG-H codec. In embodiments,
the codec is an H.263/MPEG-4 codec.
[0056] According to some embodiments of the present disclosure, a
method for monitoring an industrial setting using a sensor kit
having a plurality of sensors and an edge device including a
processing system is disclosed. The method includes: receiving, by
the processing system, reporting packets from one or more
respective sensors of the plurality of sensors, wherein each
reporting packet includes routing data and one or more instances of
sensor data; generating, by the processing system, a block of media
content frames, wherein each media content frame includes a
plurality of frame values, each frame value being indicative of a
respective instance of sensor data; compressing, by the processing
system, the block of media content frames using a media codec to
obtain a compressed block; generating, by the processing system,
one or more server kit packets based on the compressed block; and
transmitting, by the processing system, the one or more server kit
packets to a backend system via a public network. In some
embodiments, the sensor kit includes a gateway device configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device.
In embodiments, the gateway device includes a satellite terminal
device that is configured to transmit the sensor kit packets to a
satellite that routes the sensor kits to the public network. In
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0057] In embodiments, receiving the reporting packets from the one
or more respective sensors is performed using a first communication
device that receives reporting packets from the plurality of
sensors via a self-configuring sensor kit network and transmitting
the sensor kit packets to the backend system is performed using a
second communication device. In some of these embodiments, the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network. In some embodiments, the method further includes
capturing, by the plurality of sensors, sensor data; and
transmitting, by the plurality of sensors, the sensor data to the
edge device via the self-configuring sensor kit network. In some
embodiments, transmitting the sensor data via the self-configuring
sensor kit network includes directly transmitting, by each sensor
of the plurality of sensors, instances of sensor data with the edge
device using a short-range communication protocol, wherein the
self-configuring sensor kit network is a star network. In
embodiments, the method further includes initiating, by the
processing system, configuration of the self-configuring sensor kit
network.
[0058] In some embodiments, the self-configuring sensor kit network
is a mesh network and each sensor of the plurality of sensors
includes a communication device. In some of these embodiments, the
method further includes establishing, by the communication device
of each sensor of the plurality of sensors, a communication channel
with at least one other sensor of the plurality of sensors;
receiving, by at least one sensor of the plurality of sensors,
instances of sensor data from one or more other sensors of the
plurality of sensors; and routing, by the at least one sensor of
the plurality of sensors, the received instances of the sensor data
towards the edge device.
[0059] In some embodiments, the self-configuring sensor kit network
is a hierarchical network and the sensor kit includes one or more
collection devices. In some of these embodiments, the method
further includes receiving, by at least one collection device of
the plurality of collection devices, reporting packets from one or
more sensors of the plurality of sensors; and routing, by the at
least one collection device of the plurality of collection devices,
the reporting packets to the edge device.
[0060] In some embodiments, the method further includes storing, by
one or more storage devices of the edge device, instances of sensor
data captured by the plurality of sensors of the sensor kit.
[0061] In embodiments, the edge device further includes one or more
storage devices that store a model data store that stores one or
more machine-learned models that are each trained to predict or
classify a condition of an industrial component of the industrial
setting and/or the industrial setting based on a set of features
that are derived from instances of sensor data captured by one or
more of the plurality of sensors. In some of these embodiments, the
method further includes: generating, by the processing system, a
feature vector based on one or more instances of sensor data
received from one or more sensors of the plurality of sensors;
inputting, by the processing system, the feature vector to the
machine-learned model to obtain a prediction or classification
relating to a condition of a particular industrial component of the
industrial setting or the industrial setting and a degree of
confidence corresponding to the prediction or classification; and
selecting the media codec used to compress the block of media
content frames based on the classification or prediction. In some
embodiments, selecting the media codec includes selecting a lossy
codec in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial setting and the industrial setting
that collectively indicate that there are likely no issues relating
to any industrial component of the industrial setting and the
industrial setting. In embodiments, selecting the media codec
includes selecting a lossless codec in response to obtaining a
prediction or classification relating to a condition of a
particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting.
[0062] In some embodiments, the method further includes:
generating, by the processing system, a feature vector based on one
or more instances of sensor data received from one or more sensors
of the plurality of sensors; inputting, by the processing system,
the feature vector to the machine-learned model to obtain a
prediction or classification relating to a condition of a
particular industrial component of the industrial setting or the
industrial setting and a degree of confidence corresponding to the
prediction or classification; and selectively storing, by the
processing system, the one or more instances of sensor data in the
storage device of the edge device based on the prediction or
classification. In embodiments, selectively storing the one or more
instances of sensor data in the storage device includes storing the
one or more instances of sensor data in the storage device with an
expiry such that the one or more instances of sensor data are
purged from the storage device in accordance with the expiry,
wherein storing the one or more instances of sensor data in the
storage device with an expiry is performed in response to obtaining
one or more predictions or classifications relating to conditions
of respective industrial components of the industrial setting and
the industrial setting that collectively indicate that there are
likely no issues relating to any industrial component of the
industrial setting and the industrial setting. In some embodiments,
selectively storing the one or more instances of sensor data in the
storage device includes storing the one or more instances of sensor
data in the storage device indefinitely in response to obtaining a
prediction or classification relating to a condition of a
particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting.
[0063] In some embodiments, generating the block of media content
frames includes: normalizing, by the processing system, for each
instance of sensor data that is to be included in a media content
frame, the instance of sensor data into a respective normalized
media content frame value that is within of range of media content
frame values that are permitted by an encoding standard
corresponding to the media content frame; and embedding, by the
processing system, each respective normalized media content frame
value into the media content frame. In some of these embodiments,
each media content frame is a video frame including a plurality of
pixels and the respective normalized media frame values are pixel
values. In embodiments, embedding each respective normalized media
content frame value into the media content frame includes:
determining, by the processing system, a pixel of the plurality of
pixels corresponding to the respective normalized media content
frame based on a mapping that maps respective sensors of the
plurality of sensors to respective pixels of the plurality of
pixels; and setting a value of the determined pixel equal to the
respective normalized media content frame value. In some
embodiments, the codec is an H.264/MPEG-4 codec. In some
embodiments, the codec is an H.265/MPEG-H codec. In some
embodiments, the codec is an H.263/MPEG-4 codec.
[0064] In embodiments, the plurality of sensors includes a first
set of sensors of a first sensor type and a second set of sensors
of a second sensor type.
[0065] According to some embodiments of the present disclosure, a
system is disclosed. The system includes a backend system and a
sensor kit configured to monitor an industrial setting, the sensor
kit. The sensor kit includes a plurality of sensors that capture
sensor data and transmit the sensor data via a self-configuring
sensor kit network, wherein the plurality of sensors includes one
or more sensors of a first sensor type and one or more sensors of a
second sensor type, wherein at least one sensor of the plurality of
sensors includes: a sensing component that captures sensor
measurements and outputs instances of sensor data; a processing
unit that generates reporting packets based on one or more
instances of sensor data and outputs the reporting packets, wherein
each reporting packet includes routing data and one or more
instances of sensor data; and a communication device configured to
receive reporting packets from the processing unit and to transmit
the reporting packets to the edge device via the self-configuring
sensor kit network in accordance with a first communication
protocol. The edge device includes a communication system having: a
first communication device that receives reporting packets from the
plurality of sensors via the self-configuring sensor kit network;
and a second communication device that transmits sensor kit packets
to a backend system via a public network. The edge device includes
a processing system having one or more processors that execute
computer-executable instructions that cause the processing system
to: receive the reporting packets from the communication system;
perform one or more edge operations on the instances of sensor data
in the reporting packets; generate the sensor kit packets based on
the instances of sensor data, wherein each sensor kit packet
includes at least one instance of sensor data; and output the
sensor kits packets to the communication system, wherein the
communication system transmits the reporting packets to the backend
system via the public network. The backend system includes a
backend storage system that stores a sensor kit data store that
stores sensor data received from one or more respective sensor
kits, including the sensor kit; and a backend processing system
having one or more processors that execute computer-executable
instructions that cause the backend processing system to: receive
the sensor kit packets from the sensor kit; determine sensor data
collected by the sensor kit based on the sensor kit packets;
perform one or more backend operations on the sensor data collected
by the sensor kit; and store the sensor data collected by the
sensor kit in the sensor kit data store.
[0066] In some embodiments, the sensor kit further includes a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device. In some of these embodiments, the
gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. Alternatively, in
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0067] In some embodiments, the second communication device of the
edge device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network.
[0068] In embodiments, the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit.
[0069] In embodiments, the edge device further includes one or more
storage devices that store a model data store that stores one or
more machine-learned models that are each trained to predict or
classify a condition of an industrial component of the industrial
setting and/or the industrial setting based on a set of features
that are derived from instances of sensor data captured by one or
more of the plurality of sensors. In some of these embodiments,
performing one or more edge operations includes: generating a
feature vector based on one or more instances of sensor data
received from one or more sensors of the plurality of sensors;
inputting the feature vector to the machine-learned model to obtain
a prediction or classification relating to a condition of a
particular industrial component of the industrial setting or the
industrial setting and a degree of confidence corresponding to the
prediction or classification; and selectively encoding the one or
more instances of sensor data prior to transmission to the backend
system based on the condition or prediction. In some embodiments,
selectively encoding the one or more instances of sensor data
includes: in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial setting and the industrial setting
that collectively indicate that there are likely no issues relating
to any industrial component of the industrial setting and the
industrial setting, compressing the one or more instances of sensor
data using a lossy codec. In some embodiments, compressing the one
or more instances of sensor data using the lossy codec includes:
normalizing the one or more instances of sensor data into
respective pixel values; encoding the respective pixel values into
a video frame; and compressing a block of video frames using the
lossy codec to obtain a compressed block of frames, wherein the
lossy codec is a video codec and the block of video frames includes
the video frame and one or more other video frames that include
normalized pixel values of other instances of sensor data. In
embodiments, the backend system receives the compressed block of
frames in one or more sensor kit packets and determines the sensor
data collected by the sensor kit by decompressing the compressed
block of frames using the lossy codec. In some embodiments,
selectively encoding the one or more instances of sensor data
includes, in response to obtaining a prediction or classification
relating to a condition of a particular industrial component or the
industrial setting that indicates that there is likely an issue
relating to the particular industrial component or the industrial
setting, compressing the one or more instances of sensor data using
a lossless codec. In embodiments, selectively encoding the one or
more instances of sensor data includes, in response to obtaining a
prediction or classification relating to a condition of a
particular industrial component or the industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the industrial setting, refraining from
compressing the one or more instances of sensor data. In
embodiments, selectively encoding the one or more instances of
sensor data includes selecting a stream of sensor data instances
for uncompressed transmission. In embodiments, performing one or
more edge operations includes: generating a feature vector based on
one or more instances of sensor data received from one or more
sensors of the plurality of sensors; inputting the feature vector
to the machine-learned model to obtain a prediction or
classification relating to a condition of a particular industrial
component of the industrial setting or the industrial setting and a
degree of confidence corresponding to the prediction or
classification; and selectively storing the one or more instances
of sensor data in a storage device of the edge device based on the
prediction or classification. In some of these embodiments,
selectively storing the one or more instances of sensor data
includes, in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial setting and the industrial setting
that collectively indicate that there are likely no issues relating
to any industrial component of the industrial setting and the
industrial setting, storing the one or more instances of sensor
data in the storage device with an expiry, such that the one or
more instances of sensor data are purged from the storage device in
accordance with the expiry. In some embodiments, selectively
storing the one or more instances of sensor data includes, in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the industrial setting,
storing the one or more instances of sensor data in the storage
device indefinitely.
[0070] In embodiments, the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In some of
these embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to initiate
configuration of the self-configuring sensor kit network.
[0071] In some embodiments, the self-configuring sensor kit network
is a mesh network such that: the communication device of each
sensor of the plurality of sensors is configured to establish a
communication channel with at least one other sensor of the
plurality of sensors, and at least one sensor of the plurality of
sensors is configured to receive instances of sensor data from one
or more other sensors of the plurality of sensors and to route the
received instances of the sensor data towards the edge device. In
some of these embodiments, the computer-executable instructions
further cause the one or more processors of the edge device to
initiate configuration of the self-configuring sensor kit network,
wherein the plurality of sensors form the mesh network in response
to the edge device initiating configuration of the self-configuring
sensor kit network.
[0072] In embodiments, the self-configuring sensor kit network is a
hierarchical network. In some of these embodiments, the sensor kit
includes one or more collection devices configured to receive
reporting packets from one or more sensors of the plurality of
sensors and route the reporting packets to the edge device.
[0073] In embodiments, the backend operations include performing
one or more analytics tasks using the sensor data; performing one
or more artificial intelligence tasks using the sensor data;
issuing a notification to a human user associated with the
industrial setting based on the sensor data; and/or controlling at
least one component of the industrial setting based on the sensor
data.
[0074] According to some embodiments of the present disclosure, a
method for monitoring an industrial setting using a sensor kit in
communication with a backend system, the sensor kit including a
plurality of sensors and an edge device is disclosed. The method
includes: receiving, by an edge processing system of the edge
device, reporting packets from one or more respective sensors of
the plurality of sensors, wherein each reporting packet includes
routing data and one or more instances of sensor data; performing,
by the edge processing system, one or more edge operations on the
instances of sensor data in the reporting packets; generating, by
the edge processing system, a plurality of sensor kit packets based
on the instances of sensor data, wherein each sensor kit packet
includes at least one instance of sensor data; transmitting, by the
edge processing system, the sensor kit packets to the backend
system via a public network; receiving, by a backend processing
system of the backend system, the sensor kit packets from the
sensor kit via the public network; determining, by the backend
processing system, the sensor data collected by the sensor kit
based on the sensor kit packets; performing, by the backend
processing system, one or more backend operations on the sensor
data collected by the sensor kit; and storing, by the backend
processing system, the sensor data collected by the sensor kit in a
sensor kit data store residing in a backend storage system of the
backend system. In some embodiments, the sensor kit further
includes a gateway device, wherein the gateway device is configured
to receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device.
In some embodiments, the gateway device includes a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network. In embodiments, the gateway device includes a cellular
chipset that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0075] In embodiments, receiving the reporting packets from the one
or more respective sensors is performed using a first communication
device of the edge device that receives reporting packets from the
plurality of sensors via a self-configuring sensor kit network and
transmitting the sensor kit packets to the backend system is
performed using a second communication device of the edge device.
In some of these embodiments, the second communication device of
the edge device is a satellite terminal device that is configured
to transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network. In embodiments, the method
further includes capturing, by the plurality of sensors, sensor
data; and transmitting, by the plurality of sensors, the sensor
data to the edge device via the self-configuring sensor kit
network. In some embodiments, transmitting the sensor data via the
self-configuring sensor kit network includes directly transmitting,
by each sensor of the plurality of sensors, instances of sensor
data with the edge device using a short-range communication
protocol, wherein the self-configuring sensor kit network is a star
network. In embodiments, the method further includes initiating, by
the edge processing system, configuration of the self-configuring
sensor kit network. In some embodiments, the self-configuring
sensor kit network is a mesh network and each sensor of the
plurality of sensors includes a communication device. In some
embodiments, the method further includes: establishing, by the
communication device of each sensor of the plurality of sensors, a
communication channel with at least one other sensor of the
plurality of sensors; receiving, by at least one sensor of the
plurality of sensors, instances of sensor data from one or more
other sensors of the plurality of sensors; and routing, by the at
least one sensor of the plurality of sensors, the received
instances of the sensor data towards the edge device.
[0076] In some embodiments, the self-configuring sensor kit network
is a hierarchical network and the sensor kit includes one or more
collection devices. In some of these embodiments, the method
further includes: receiving, by at least one collection device of
the plurality of collection devices, reporting packets from one or
more sensors of the plurality of sensors; and routing, by the at
least one collection device of the plurality of collection devices,
the reporting packets to the edge device.
[0077] In embodiments, the method further includes storing, by one
or more storage devices of the edge device, instances of sensor
data captured by the plurality of sensors of the sensor kit.
[0078] In some embodiments, the edge device further includes one or
more storage devices that store a model data store that stores one
or more machine-learned models that are each trained to predict or
classify a condition of an industrial component of the industrial
setting and/or the industrial setting based on a set of features
that are derived from instances of sensor data captured by one or
more of the plurality of sensors. In some of these embodiments,
performing one or more edge operations includes: generating, by the
edge processing system, a feature vector based on one or more
instances of sensor data received from one or more sensors of the
plurality of sensors; inputting, by the edge processing system, the
feature vector to the machine-learned model to obtain a prediction
or classification relating to a condition of a particular
industrial component of the industrial setting or the industrial
setting and a degree of confidence corresponding to the prediction
or classification; and selectively encoding, by the edge processing
system, the one or more instances of sensor data prior to
transmission to the backend system based on the prediction or
classification. In some embodiments, selectively encoding the one
or more instances of sensor data includes compressing, by the edge
processing system, the one or more instances of sensor data using a
lossy codec in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial setting and the industrial setting
that collectively indicate that there are likely no issues relating
to any industrial component of the industrial setting and the
industrial setting. In some embodiments, compressing the one or
more instances of sensor data using a lossy codec includes:
normalizing, by the edge processing system, the one or more
instances of sensor data into respective pixel values; encoding, by
the edge processing system, the respective pixel values into a
media content frame; and compressing, by the edge processing
system, a block of media content frames using the lossy codec to
obtain a compressed block, wherein the lossy codec is a video codec
and the compressed block includes the media content frame and one
or more other media content frames that include normalized pixel
values of other instances of sensor data. In embodiments, the
backend system receives the compressed block in one or more sensor
kit packets and determines the sensor data collected by the sensor
kit by decompressing the compressed block using the lossy
codec.
[0079] In some embodiments, selectively encoding the one or more
instances of sensor data includes compressing, by the edge
processing system, the one or more instances of sensor data using a
lossless codec in response to obtaining a prediction or
classification relating to a condition of a particular industrial
component or the industrial setting that indicates that there is
likely an issue relating to the particular industrial component or
the industrial setting. In embodiments, selectively encoding the
one or more instances of sensor data includes refraining, by the
edge processing system, from compressing the one or more instances
of sensor data in response to obtaining a prediction or
classification relating to a condition of a particular industrial
component or the industrial setting that indicates that there is
likely an issue relating to the particular industrial component or
the industrial setting. In some embodiments, selectively encoding
the one or more instances of sensor data includes selecting, by the
edge processing system, a stream of sensor data instances for
uncompressed transmission.
[0080] In some embodiments, performing one or more edge operations
includes: generating, by the edge processing system, a feature
vector based on one or more instances of sensor data received from
one or more sensors of the plurality of sensors; inputting, by the
edge processing system, the feature vector to the machine-learned
model to obtain a prediction or classification relating to a
condition of a particular industrial component of the industrial
setting or the industrial setting and a degree of confidence
corresponding to the prediction or classification; and selectively
storing, by the edge processing system, the one or more instances
of sensor data in a storage device of the one or more storage
devices based on the prediction or classification. In some
embodiments, selectively storing the one or more instances of
sensor data includes storing, by the edge processing system, the
one or more instances of sensor data in the storage device with an
expiry in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial setting and the industrial setting
that collectively indicate that there are likely no issues relating
to any industrial component of the industrial setting and the
industrial setting, wherein storing the one or more instances of
sensor data in the storage device with an expiry is performed such
that the one or more instances of sensor data are purged from the
storage device in accordance with the expiry. In some embodiments,
selectively storing the one or more instances of sensor data
includes storing, by the edge processing system, the one or more
instances of sensor data in the storage device indefinitely in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the industrial setting.
[0081] In some embodiments, the plurality of sensors includes a
first set of sensors of a first sensor type and a second set of
sensors of a second sensor type.
[0082] According to some embodiments of the present disclosure, a
sensor kit configured to monitor an indoor agricultural facility is
disclosed. The sensor kit includes an edge device and a plurality
of sensors that capture sensor data and transmit the sensor data
via a self-configuring sensor kit network, wherein the plurality of
sensors includes one or more sensors of a first sensor type and one
or more sensors of a second sensor type. At least one sensor of the
plurality of sensors includes: a sensing component that captures
sensor measurements and outputs instances of sensor data; a
processing unit that generates reporting packets based on one or
more instances of sensor data and outputs the reporting packets,
wherein each reporting packet includes routing data and one or more
instances of sensor data; and a communication device configured to
receive reporting packets from the processing unit and to transmit
the reporting packets to the edge device via the self-configuring
sensor kit network in accordance with a first communication
protocol. The plurality of sensors includes two or more sensor
types selected from the group including: light sensors, humidity
sensors, temperature sensors, carbon dioxide sensors, fan speed
sensors, weight sensors, and camera sensors. The edge device
includes a communication system having a first communication device
that receives reporting packets from the plurality of sensors via
the self-configuring sensor kit network and a second communication
device that transmits sensor kit packets to a backend system via a
public network. The edge device also includes a processing system
having one or more processors that execute computer-executable
instructions that cause the processing system to: receive the
reporting packets from the communication system, perform one or
more edge operations on the instances of sensor data in the
reporting packets; generate the sensor kit packets based on the
instances of sensor data, wherein each sensor kit packet includes
at least one instance of sensor data; and output the sensor kits
packets to the communication system, wherein the communication
system transmits the reporting packets to the backend system via
the public network.
[0083] In embodiments, the sensor kit includes an edge device and a
plurality of sensors that capture sensor data and transmit the
sensor data via a self-configuring sensor kit network. The
plurality of sensors includes one or more sensors of a first sensor
type and one or more sensors of a second sensor type. At least one
sensor of the plurality of sensors includes a sensing component
that captures sensor measurements and outputs instances of sensor
data; a processing unit that generates reporting packets based on
one or more instances of sensor data and outputs the reporting
packets, wherein each reporting packet includes routing data and
one or more instances of sensor data; and a communication device
configured to receive reporting packets from the processing unit
and to transmit the reporting packets to the edge device via the
self-configuring sensor kit network in accordance with a first
communication protocol.
[0084] In embodiments, the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit.
[0085] In embodiments, the edge device further includes one or more
storage devices that store a model data store that stores one or
more machine-learned models that are each trained to predict or
classify a condition of a component of the indoor agricultural
setting and/or the indoor agricultural setting based on a set of
features that are derived from instances of sensor data captured by
one or more of the plurality of sensors. In some of these
embodiments, performing one or more edge operations includes:
generating a feature vector based on one or more instances of
sensor data received from one or more sensors of the plurality of
sensors; inputting the feature vector to the machine-learned model
to obtain a prediction or classification relating to a condition of
a particular component of the indoor agricultural setting or the
indoor agricultural setting and a degree of confidence
corresponding to the prediction or classification; and selectively
encoding the one or more instances of sensor data prior to
transmission to the backend system based on the condition or
prediction. In some embodiments, selectively encoding the one or
more instances of sensor data includes compressing the one or more
instances of sensor data using a lossy codec in response to
obtaining one or more predictions or classifications relating to
conditions of respective industrial components of the indoor
agricultural setting and the indoor agricultural setting that
collectively indicate that there are likely no issues relating to
any component of the indoor agricultural setting and the indoor
agricultural setting. In some embodiments, compressing the one or
more instances of sensor data using the lossy codec includes:
normalizing the one or more instances of sensor data into
respective pixel values; encoding the respective pixel values into
a video frame; and compressing a block of video frames using the
lossy codec, wherein the lossy codec is a video codec and the block
of video frames includes the video frame and one or more other
video frames that include normalized pixel values of other
instances of sensor data. In some embodiments, selectively encoding
the one or more instances of sensor data includes: compressing the
one or more instances of sensor data using a lossless codec in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the industrial setting. In
embodiments, selectively encoding the one or more instances of
sensor data includes refraining from compressing the one or more
instances of sensor data in response to obtaining a prediction or
classification relating to a condition of a particular component or
the indoor agricultural setting that indicates that there is likely
an issue relating to the particular component or the indoor
agricultural setting. In embodiments, performing one or more edge
operations includes: generating a feature vector based on one or
more instances of sensor data received from one or more sensors of
the plurality of sensors; inputting the feature vector to the
machine-learned model to obtain a prediction or classification
relating to a condition of a particular component of the indoor
agricultural setting or the indoor agricultural setting and a
degree of confidence corresponding to the prediction or
classification; and selectively storing the one or more instances
of sensor data in a storage device of the edge device based on the
prediction or classification. In some of these embodiments,
selectively storing the one or more instances of sensor data
includes storing the one or more instances of sensor data in the
storage device with an expiry in response to obtaining one or more
predictions or classifications relating to conditions of respective
industrial components of the indoor agricultural setting and the
indoor agricultural setting that collectively indicate that there
are likely no issues relating to any component of the indoor
agricultural setting and the indoor agricultural setting, such that
the one or more instances of sensor data are purged from the
storage device in accordance with the expiry. In some embodiments,
selectively storing the one or more instances of sensor data
includes storing the one or more instances of sensor data in the
storage device indefinitely in response to obtaining a prediction
or classification relating to a condition of a particular
industrial component or the industrial setting that indicates that
there is likely an issue relating to the particular component or
the indoor agricultural setting.
[0086] In embodiments, the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In some of
these embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to initiate
configuration of the self-configuring sensor kit network.
[0087] In embodiments, the self-configuring sensor kit network is a
mesh network such that: the communication device of each sensor of
the plurality of sensors is configured to establish a communication
channel with at least one other sensor of the plurality of sensors;
and at least one sensor of the plurality of sensors is configured
to receive instances of sensor data from one or more other sensors
of the plurality of sensors and to route the received instances of
the sensor data towards the edge device. In some of these
embodiments, the computer-executable instructions further cause the
one or more processors of the edge device to initiate configuration
of the self-configuring sensor kit network, wherein the plurality
of sensors form the mesh network in response to the edge device
initiating configuration of the self-configuring sensor kit
network.
[0088] In embodiments, the self-configuring sensor kit network is a
hierarchical network. In some of these embodiments, the sensor kit
further includes one or more collection devices configured to
receive reporting packets from one or more sensors of the plurality
of sensors and route the reporting packets to the edge device. In
embodiments, each collection device is installed in a different
respective room of the indoor agricultural setting and collects
sensor data from sensors of the plurality sensors that are deployed
in the respective room.
[0089] According to some embodiments of the present disclosure, a
sensor kit configured to monitor an indoor agricultural setting is
disclosed. The sensor kit includes an edge device and a plurality
of sensors that capture sensor data and transmit the sensor data
via a self-configuring sensor kit network, wherein the plurality of
sensors includes one or more sensors of a first sensor type and one
or more sensors of a second sensor type. At least one sensor of the
plurality of sensors includes: a sensing component that captures
sensor measurements and outputs instances of sensor data; a
processing unit that generates reporting packets based on one or
more instances of sensor data and outputs the reporting packets,
wherein each reporting packet includes routing data and one or more
instances of sensor data; and a communication device configured to
receive reporting packets from the processing unit and to transmit
the reporting packets to the edge device via the self-configuring
sensor kit network in accordance with a first communication
protocol. The plurality of sensors includes two or more sensor
types selected from the group including: infrared sensors, ground
penetrating sensors, light sensors, humidity sensors, temperature
sensors, chemical sensors, fan speed sensors, rotational speed
sensors, weight sensors, and camera sensors. The edge device
includes a communication system having a first communication device
that receives reporting packets from the plurality of sensors via
the self-configuring sensor kit network and a second communication
device that transmits sensor kit packets to a backend system via a
public network. The edge device further includes a processing
system having one or more processors that execute
computer-executable instructions that cause the processing system
to: receive the reporting packets from the communication system;
perform one or more edge operations on the instances of sensor data
in the reporting packets; generate the sensor kit packets based on
the instances of sensor data, wherein each sensor kit packet
includes at least one instance of sensor data; and output the
sensor kits packets to the communication system, wherein the
communication system transmits the reporting packets to the backend
system via the public network.
[0090] In some embodiments, the sensor kit further includes a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device. In some of these embodiments, the
gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. Alternatively, in
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0091] In some embodiments, the second communication device of the
edge device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network.
[0092] In embodiments, the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit.
[0093] In embodiments, the edge device further includes one or more
storage devices that store a model data store that stores one or
more machine-learned models that are each trained to predict or
classify a condition of a component of the indoor agricultural
setting and/or the indoor agricultural setting based on a set of
features that are derived from instances of sensor data captured by
one or more of the plurality of sensors. In some embodiments,
performing one or more edge operations includes: generating a
feature vector based on one or more instances of sensor data
received from one or more sensors of the plurality of sensors;
inputting the feature vector to the machine-learned model to obtain
a prediction or classification relating to a condition of a
particular component of the indoor agricultural setting or the
indoor agricultural and a degree of confidence corresponding to the
prediction or classification; and selectively encoding the one or
more instances of sensor data prior to transmission to the backend
system based on the condition or prediction.
[0094] In embodiments, selectively encoding the one or more
instances of sensor data includes compressing the one or more
instances of sensor data using a lossy codec in response to
obtaining one or more predictions or classifications relating to
conditions of respective components of the indoor agricultural
setting and the indoor agricultural setting that collectively
indicate that there are likely no issues relating to any component
of the indoor agricultural setting and the indoor agricultural
setting. In embodiments, compressing the one or more instances of
sensor data using the lossy codec includes: normalizing the one or
more instances of sensor data into respective pixel values;
encoding the respective pixel values into a video frame; and
compressing a block of video frames using the lossy codec, wherein
the lossy codec is a video codec and the block of video frames
includes the video frame and one or more other video frames that
include normalized pixel values of other instances of sensor data.
In embodiments, selectively encoding the one or more instances of
sensor data includes compressing the one or more instances of
sensor data using a lossless codec in response to obtaining a
prediction or classification relating to a condition of a
particular component or the indoor agricultural setting that
indicates that there is likely an issue relating to the particular
component or the indoor agricultural setting. In embodiments,
selectively encoding the one or more instances of sensor data
includes refraining from compressing the one or more instances of
sensor data in response to obtaining a prediction or classification
relating to a condition of a particular component or the indoor
agricultural setting that indicates that there is likely an issue
relating to the particular component or the indoor agricultural
setting.
[0095] In some embodiments, performing one or more edge operations
includes: generating a feature vector based on one or more
instances of sensor data received from one or more sensors of the
plurality of sensors; inputting the feature vector to the
machine-learned model to obtain a prediction or classification
relating to a condition of a particular component of the indoor
agricultural setting or the indoor agricultural setting and a
degree of confidence corresponding to the prediction or
classification; and selectively storing the one or more instances
of sensor data in a storage device of the edge device based on the
prediction or classification. In embodiments, selectively storing
the one or more instances of sensor data includes storing the one
or more instances of sensor data in the storage device with an
expiry in response to obtaining one or more predictions or
classifications relating to conditions of respective components of
the indoor agricultural setting and the indoor agricultural setting
that collectively indicate that there are likely no issues relating
to any component of the indoor agricultural setting and the indoor
agricultural setting, such that the one or more instances of sensor
data are purged from the storage device in accordance with the
expiry. In embodiments, selectively storing the one or more
instances of sensor data includes storing the one or more instances
of sensor data in the storage device indefinitely in response to
obtaining a prediction or classification relating to a condition of
a particular component or the indoor agricultural setting that
indicates that there is likely an issue relating to the particular
component or the indoor agricultural setting.
[0096] In some embodiments, the plurality of sensors includes a
first set of sensors of a first sensor type and a second set of
sensors of a second sensor type selected from the group including:
light sensors, humidity sensors, temperature sensors, carbon
dioxide sensors, fan speed sensors, weight sensors, and camera
sensors.
[0097] According to some embodiments of the present disclosure, a
sensor kit configured to monitor a pipeline setting is disclosed.
The sensor kit includes an edge device and a plurality of sensors
that capture sensor data and transmit the sensor data via a
self-configuring sensor kit network. The plurality of sensors
includes one or more sensors of a first sensor type and one or more
sensors of a second sensor type. At least one sensor of the
plurality of sensors includes: a sensing component that captures
sensor measurements and outputs instances of sensor data; a
processing unit that generates reporting packets based on one or
more instances of sensor data and outputs the reporting packets,
wherein each reporting packet includes routing data and one or more
instances of sensor data; and a communication device configured to
receive reporting packets from the processing unit and to transmit
the reporting packets to the edge device via the self-configuring
sensor kit network in accordance with a first communication
protocol. The plurality of sensors includes two or more sensor
types selected from the group including: infrared sensors, metal
penetrating sensors, concrete penetrating sensors, light sensors,
strain sensors, rust sensors, biological sensors, humidity sensors,
temperature sensors, chemical sensors, valve integrity sensors,
vibration sensors, flow sensors, cavitation sensors, pressure
sensors, weight sensors, and camera sensors. The edge device
includes a communication system having: a first communication
device that receives reporting packets from the plurality of
sensors via the self-configuring sensor kit network and a second
communication device that transmits sensor kit packets to a backend
system via a public network. The edge device further includes a
processing system having one or more processors that execute
computer-executable instructions that cause the processing system
to: receive the reporting packets from the communication system;
perform one or more edge operations on the instances of sensor data
in the reporting packets; generate the sensor kit packets based on
the instances of sensor data, wherein each sensor kit packet
includes at least one instance of sensor data; and output the
sensor kits packets to the communication system, wherein the
communication system transmits the reporting packets to the backend
system via the public network.
[0098] In some embodiments, the sensor kit further includes a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device. In some of these embodiments, the
gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. Alternatively, in
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0099] In some embodiments, the second communication device of the
edge device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network.
[0100] In embodiments, the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit.
[0101] In embodiments, the edge device further includes one or more
storage devices that store a model data store that stores one or
more machine-learned models that are each trained to predict or
classify a condition of a pipeline component of the pipeline
setting and/or the pipeline setting based on a set of features that
are derived from instances of sensor data captured by one or more
of the plurality of sensors. In some of these embodiments,
performing one or more edge operations includes: generating a
feature vector based on one or more instances of sensor data
received from one or more sensors of the plurality of sensors;
inputting the feature vector to the machine-learned model to obtain
a prediction or classification relating to a condition of a
particular pipeline component of the pipeline setting or the
pipeline setting and a degree of confidence corresponding to the
prediction or classification; and selectively encoding the one or
more instances of sensor data prior to transmission to the backend
system based on the condition or prediction. In embodiments,
selectively encoding the one or more instances of sensor data
includes compressing the one or more instances of sensor data using
a lossy codec in response to obtaining one or more predictions or
classifications relating to conditions of respective pipeline
components of the pipeline setting and the pipeline setting that
collectively indicate that there are likely no issues relating to
any pipeline component of the pipeline setting and the pipeline
setting. In embodiments, compressing the one or more instances of
sensor data using the lossy codec includes: normalizing the one or
more instances of sensor data into respective pixel values;
encoding the respective pixel values into a video frame; and
compressing a block of video frames using the lossy codec, wherein
the lossy codec is a video codec and the block of video frames
includes the video frame and one or more other video frames that
include normalized pixel values of other instances of sensor data.
In embodiments, selectively encoding the one or more instances of
sensor data includes compressing the one or more instances of
sensor data using a lossless codec in response to obtaining a
prediction or classification relating to a condition of a
particular pipeline component or the pipeline setting that
indicates that there is likely an issue relating to the particular
pipeline component or the pipeline setting. In embodiments,
selectively encoding the one or more instances of sensor data
includes refraining from compressing the one or more instances of
sensor data in response to obtaining a prediction or classification
relating to a condition of a particular pipeline component or the
pipeline setting that indicates that there is likely an issue
relating to the particular pipeline component or the pipeline
setting. In embodiments, performing one or more edge operations
includes generating a feature vector based on one or more instances
of sensor data received from one or more sensors of the plurality
of sensors; inputting the feature vector to the machine-learned
model to obtain a prediction or classification relating to a
condition of a particular pipeline component of the pipeline
setting or the pipeline setting and a degree of confidence
corresponding to the prediction or classification; and selectively
storing the one or more instances of sensor data in a storage
device of the edge device based on the prediction or
classification. In embodiments, selectively storing the one or more
instances of sensor data includes storing the one or more instances
of sensor data in the storage device with an expiry in response to
obtaining one or more predictions or classifications relating to
conditions of respective pipeline components of the pipeline
setting and the pipeline setting that collectively indicate that
there are likely no issues relating to any pipeline component of
the pipeline setting and the pipeline setting, such that the one or
more instances of sensor data are purged from the storage device in
accordance with the expiry. In embodiments, selectively storing the
one or more instances of sensor data includes storing the one or
more instances of sensor data in the storage device indefinitely in
response to obtaining a prediction or classification relating to a
condition of a particular pipeline component or the pipeline
setting that indicates that there is likely an issue relating to
the particular pipeline component or the pipeline setting.
[0102] In embodiments, the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In some of
these embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to initiate
configuration of the self-configuring sensor kit network.
[0103] In embodiments, the self-configuring sensor kit network is a
mesh network such that: the communication device of each sensor of
the plurality of sensors is configured to establish a communication
channel with at least one other sensor of the plurality of sensors;
and at least one sensor of the plurality of sensors is configured
to receive instances of sensor data from one or more other sensors
of the plurality of sensors and to route the received instances of
the sensor data towards the edge device. In some of these
embodiments, the computer-executable instructions further cause the
one or more processors of the edge device to initiate configuration
of the self-configuring sensor kit network, wherein the plurality
of sensors form the mesh network in response to the edge device
initiating configuration of the self-configuring sensor kit
network.
[0104] In embodiments, the self-configuring sensor kit network is a
hierarchical network. In some of these embodiments, the sensor kit
further includes one or more collection devices configured to
receive reporting packets from one or more sensors of the plurality
of sensors and route the reporting packets to the edge device. In
embodiments, each collection device is installed in a different
respective section of the pipeline setting and collects sensor data
from sensors of the plurality sensors that are deployed in the
respective room.
[0105] According to some embodiments of the present disclosure, a
method of monitoring a pipeline setting using a sensor kit
including an edge device and a plurality of sensors is disclosed.
The method includes: receiving, by an edge processing system of the
edge device, reporting packets from a plurality of sensors via a
self-configuring sensor kit network, each reporting packet
containing routing data and one or more instances of sensor data
captured by a respective sensor of the plurality of sensors,
wherein the plurality of sensors includes two or more sensor types
selected from the group including: light sensors, humidity sensors,
temperature sensors, carbon dioxide sensors, fan speed sensors,
weight sensors, and camera sensors; performing, by the edge
processing system, one or more edge operations on the instances of
sensor data in the reporting packets; generating, by the edge
processing system, one or more edge operations on the instances of
sensor data in the reporting packets; and transmitting, by the edge
processing system, the sensor kit packets to an edge communication
system of the edge device, wherein the edge communication system
transmits the reporting packets to a backend system via a public
network. In some embodiments, the sensor kit further includes a
gateway device, wherein the gateway device is configured to receive
sensor kit packets from the edge device via a wired communication
link and transmit the sensor kit packets to the backend system via
the public network on behalf of the edge device. In embodiments,
the gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. In some embodiments,
the gateway device includes a cellular chipset that is
pre-configured to transmit sensor kit packets to a cellphone tower
of a preselected cellular provider. In embodiments, receiving the
reporting packets from the one or more respective sensors is
performed using a first communication device of the edge device
that receives reporting packets from the plurality of sensors via a
self-configuring sensor kit network and transmitting the sensor kit
packets to the backend system is performed using a second
communication device of the edge device. In some embodiments, the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network.
[0106] In some embodiments, the method further includes capturing,
by the plurality of sensors, sensor data; and transmitting, by the
plurality of sensors, the sensor data to the edge device via the
self-configuring sensor kit network. In some of these embodiments,
transmitting the sensor data via the self-configuring sensor kit
network includes directly transmitting, by each sensor of the
plurality of sensors, instances of sensor data with the edge device
using a short-range communication protocol, wherein the
self-configuring sensor kit network is a star network. In some
embodiments, the method further includes initiating, by the edge
processing system, configuration of the self-configuring sensor kit
network.
[0107] In embodiments, the self-configuring sensor kit network is a
mesh network and each sensor of the plurality of sensors includes a
communication device. In some of these embodiments, the method
further includes: establishing, by the communication device of each
sensor of the plurality of sensors, a communication channel with at
least one other sensor of the plurality of sensors; receiving, by
at least one sensor of the plurality of sensors, instances of
sensor data from one or more other sensors of the plurality of
sensors; and routing, by the at least one sensor of the plurality
of sensors, the received instances of the sensor data towards the
edge device.
[0108] In some embodiments, the self-configuring sensor kit network
is a hierarchical network and the sensor kit includes one or more
collection devices. In some of these embodiments, the method
further includes: receiving, by at least one collection device of
the plurality of collection devices, reporting packets from one or
more sensors of the plurality of sensors; and routing, by the at
least one collection device of the plurality of collection devices,
the reporting packets to the edge device. In some embodiments, each
collection device is installed in a different respective section of
the pipeline setting and collects sensor data from sensors of the
plurality sensors that are deployed in the respective room.
[0109] In some embodiments, the method further includes storing, by
one or more storage devices of the edge device, instances of sensor
data captured by the plurality of sensors of the sensor kit. In
embodiments, the edge device further includes one or more storage
devices that store a model data store that stores one or more
machine-learned models that are each trained to predict or classify
a condition of a component of the agricultural setting and/or the
agricultural setting based on a set of features that are derived
from instances of sensor data captured by one or more of the
plurality of sensors.
[0110] In some embodiments, performing one or more edge operations
includes: generating, by the edge processing system, a feature
vector based on one or more instances of sensor data received from
one or more sensors of the plurality of sensors; inputting, by the
edge processing system, the feature vector to the machine-learned
model to obtain a prediction or classification relating to a
condition of a particular component of the agricultural setting or
the agricultural setting and a degree of confidence corresponding
to the prediction or classification; and selectively encoding, by
the edge processing system, the one or more instances of sensor
data prior to transmission to the backend system based on the
prediction or classification. In some of these embodiments,
selectively encoding the one or more instances of sensor data
includes compressing, by the edge processing system, the one or
more instances of sensor data using a lossy codec in response to
obtaining one or more predictions or classifications relating to
conditions of respective components of the agricultural setting and
the agricultural setting that collectively indicate that there are
likely no issues relating to any component of the agricultural
setting and the agricultural setting. In some embodiments,
compressing the one or more instances of sensor data using a lossy
codec includes: normalizing, by the edge processing system, the one
or more instances of sensor data into respective pixel values;
encoding, by the edge processing system, the respective pixel
values into a media content frame; and compressing, by the edge
processing system, a block of media content frames using the lossy
codec to obtain a compressed block, wherein the lossy codec is a
video codec and the compressed block includes the media content
frame and one or more other media content frames that include
normalized pixel values of other instances of sensor data. In some
embodiments, the backend system receives the compressed block in
one or more sensor kit packets and determines the sensor data
collected by the sensor kit by decompressing the compressed block
using the lossy codec.
[0111] In some embodiments, selectively encoding the one or more
instances of sensor data includes compressing, by the edge
processing system, the one or more instances of sensor data using a
lossless codec in response to obtaining a prediction or
classification relating to a condition of a particular component or
the agricultural setting that indicates that there is likely an
issue relating to the particular component or the agricultural
setting. In embodiments, encoding the one or more instances of
sensor data includes refraining, by the edge processing system,
from compressing the one or more instances of sensor data in
response to obtaining a prediction or classification relating to a
condition of a particular component or the agricultural setting
that indicates that there is likely an issue relating to the
particular component or the agricultural setting. In some
embodiments, selectively encoding the one or more instances of
sensor data includes selecting, by the edge processing system, a
stream of sensor data instances for uncompressed transmission.
[0112] In some embodiments, performing one or more edge operations
includes: generating, by the edge processing system, a feature
vector based on one or more instances of sensor data received from
one or more sensors of the plurality of sensors; inputting, by the
edge processing system, the feature vector to the machine-learned
model to obtain a prediction or classification relating to a
condition of a particular component of the agricultural setting or
the agricultural setting and a degree of confidence corresponding
to the prediction or classification; and selectively storing, by
the edge processing system, the one or more instances of sensor
data in a storage device of the one or more storage devices based
on the prediction or classification. In some of these embodiments,
selectively storing the one or more instances of sensor data
includes storing, by the edge processing system, the one or more
instances of sensor data in the storage device with an expiry in
response to obtaining one or more predictions or classifications
relating to conditions of respective components of the agricultural
setting and the agricultural setting that collectively indicate
that there are likely no issues relating to any component of the
agricultural setting and the agricultural setting, wherein storing
the one or more instances of sensor data in the storage device with
an expiry is performed such that the one or more instances of
sensor data are purged from the storage device in accordance with
the expiry. In some embodiments, selectively storing the one or
more instances of sensor data includes storing, by the edge
processing system, the one or more instances of sensor data in the
storage device indefinitely in response to obtaining a prediction
or classification relating to a condition of a particular component
or the agricultural setting that indicates that there is likely an
issue relating to the particular component or the agricultural
setting. In some embodiments, the plurality of sensors includes a
first set of sensors of a first sensor type and a second set of
sensors of a second sensor type selected from the group including:
light sensors, humidity sensors, temperature sensors, carbon
dioxide sensors, fan speed sensors, weight sensors, and camera
sensors.
[0113] According to some embodiments of the present disclosure, a
sensor kit configured to monitor an industrial manufacturing
setting is disclosed. The sensor kit includes an edge device and a
plurality of sensors that capture sensor data and transmit the
sensor data via a self-configuring sensor kit network, wherein the
plurality of sensors includes one or more sensors of a first sensor
type and one or more sensors of a second sensor type. At least one
sensor of the plurality of sensors includes a sensing component
that captures sensor measurements and outputs instances of sensor
data; a processing unit that generates reporting packets based on
one or more instances of sensor data and outputs the reporting
packets, wherein each reporting packet includes routing data and
one or more instances of sensor data; and a communication device
configured to receive reporting packets from the processing unit
and to transmit the reporting packets to the edge device via the
self-configuring sensor kit network in accordance with a first
communication protocol. The plurality of sensors includes two or
more sensor types selected from the group including: metal
penetrating sensors, concrete penetrating sensors, vibration
sensors, light sensors, strain sensors, rust sensors, biological
sensors, temperature sensors, chemical sensors, valve integrity
sensors, rotational speed sensors, vibration sensors, flow sensors,
cavitation sensors, pressure sensors, weight sensors, and camera
sensors. The edge device includes a communication system having a
first communication device that receives reporting packets from the
plurality of sensors via the self-configuring sensor kit network;
and a second communication device that transmits sensor kit packets
to a backend system via a public network. The edge device further
includes a processing system having one or more processors that
execute computer-executable instructions that cause the processing
system to: receive the reporting packets from the communication
system; perform one or more edge operations on the instances of
sensor data in the reporting packets; generate the sensor kit
packets based on the instances of sensor data, wherein each sensor
kit packet includes at least one instance of sensor data; and
output the sensor kits packets to the communication system, wherein
the communication system transmits the reporting packets to the
backend system via the public network.
[0114] In some embodiments, the sensor kit further includes a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device. In some of these embodiments, the
gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. Alternatively, in
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0115] In some embodiments, the second communication device of the
edge device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network.
[0116] In embodiments, the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit.
[0117] In embodiments, the edge device further includes one or more
storage devices that store a model data store that stores one or
more machine-learned models that are each trained to predict or
classify a condition of an industrial component of the industrial
manufacturing setting and/or the industrial manufacturing setting
based on a set of features that are derived from instances of
sensor data captured by one or more of the plurality of sensors. In
some embodiments, performing one or more edge operations includes:
generating a feature vector based on one or more instances of
sensor data received from one or more sensors of the plurality of
sensors; inputting the feature vector to the machine-learned model
to obtain a prediction or classification relating to a condition of
a particular industrial component of the industrial manufacturing
setting or the industrial manufacturing setting and a degree of
confidence corresponding to the prediction or classification; and
selectively encoding the one or more instances of sensor data prior
to transmission to the backend system based on the condition or
prediction. In some of these embodiments, selectively encoding the
one or more instances of sensor data includes compressing the one
or more instances of sensor data using a lossy codec in response to
obtaining one or more predictions or classifications relating to
conditions of respective industrial components of the industrial
manufacturing setting and the industrial manufacturing setting that
collectively indicate that there are likely no issues relating to
any industrial component of the industrial manufacturing setting
and the industrial manufacturing setting. In some embodiments,
compressing the one or more instances of sensor data using the
lossy codec includes: normalizing the one or more instances of
sensor data into respective pixel values; encoding the respective
pixel values into a video frame; and compressing a block of video
frames using the lossy codec, wherein the lossy codec is a video
codec and the block of video frames includes the video frame and
one or more other video frames that include normalized pixel values
of other instances of sensor data. In embodiments, selectively
encoding the one or more instances of sensor data includes
compressing the one or more instances of sensor data using a
lossless codec in response to obtaining a prediction or
classification relating to a condition of a particular industrial
component or the industrial manufacturing setting that indicates
that there is likely an issue relating to the particular industrial
component or the industrial manufacturing setting. In embodiments,
selectively encoding the one or more instances of sensor data
includes refraining from compressing the one or more instances of
sensor data in response to obtaining a prediction or classification
relating to a condition of a particular industrial component or the
industrial manufacturing setting that indicates that there is
likely an issue relating to the particular industrial component or
the industrial manufacturing setting. In embodiments, performing
one or more edge operations includes: generating a feature vector
based on one or more instances of sensor data received from one or
more sensors of the plurality of sensors; inputting the feature
vector to the machine-learned model to obtain a prediction or
classification relating to a condition of a particular industrial
component of the industrial manufacturing setting or the industrial
manufacturing setting and a degree of confidence corresponding to
the prediction or classification; and selectively storing the one
or more instances of sensor data in a storage device of the edge
device based on the prediction or classification. In embodiments,
selectively storing the one or more instances of sensor data
includes storing the one or more instances of sensor data in the
storage device with an expiry, such that the one or more instances
of sensor data are purged from the storage device in accordance
with the expiry in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the industrial manufacturing setting and the
industrial manufacturing setting that collectively indicate that
there are likely no issues relating to any industrial component of
the industrial manufacturing setting and the industrial
manufacturing setting. In embodiments, selectively storing the one
or more instances of sensor data includes storing the one or more
instances of sensor data in the storage device indefinitely in
response to obtaining a prediction or classification relating to a
condition of a particular industrial component or the industrial
manufacturing setting that indicates that there is likely an issue
relating to the particular industrial component or the industrial
manufacturing setting.
[0118] In embodiments, the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In some of
these embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to initiate
configuration of the self-configuring sensor kit network.
[0119] In embodiments, the self-configuring sensor kit network is a
mesh network such that: the communication device of each sensor of
the plurality of sensors is configured to establish a communication
channel with at least one other sensor of the plurality of sensors;
and at least one sensor of the plurality of sensors is configured
to receive instances of sensor data from one or more other sensors
of the plurality of sensors and to route the received instances of
the sensor data towards the edge device. In some of these
embodiments, the computer-executable instructions further cause the
one or more processors of the edge device to initiate configuration
of the self-configuring sensor kit network, wherein the plurality
of sensors form the mesh network in response to the edge device
initiating configuration of the self-configuring sensor kit
network.
[0120] In embodiments, the self-configuring sensor kit network is a
hierarchical network. In some of these embodiments, the sensor kit
further includes one or more collection devices configured to
receive reporting packets from one or more sensors of the plurality
of sensors and route the reporting packets to the edge device. In
embodiments, each collection device is installed in a different
respective room of the industrial manufacturing setting and
collects sensor data from sensors of the plurality sensors that are
deployed in the respective room.
[0121] According to some embodiments of the present disclosure, a
sensor kit configured to monitor an underwater industrial setting
is disclosed. The sensor kit includes an edge device and a
plurality of sensors that capture sensor data and transmit the
sensor data via a self-configuring sensor kit network, wherein the
plurality of sensors includes one or more sensors of a first sensor
type and one or more sensors of a second sensor type. At least one
sensor of the plurality of sensors includes: a sensing component
that captures sensor measurements and outputs instances of sensor
data; a processing unit that generates reporting packets based on
one or more instances of sensor data and outputs the reporting
packets, wherein each reporting packet includes routing data and
one or more instances of sensor data; and a communication device
configured to receive reporting packets from the processing unit
and to transmit the reporting packets to the edge device via the
self-configuring sensor kit network in accordance with a first
communication protocol. The plurality of sensors includes two or
more sensor types selected from the group including: infrared
sensors, sonar sensors, LIDAR sensors, water penetrating sensors,
light sensors, strain sensors, rust sensors, biological sensors,
temperature sensors, chemical sensors, valve integrity sensors,
vibration sensors, flow sensors, cavitation sensors, pressure
sensors, weight sensors, and camera sensors. The edge device
includes a communication system having a first communication device
that receives reporting packets from the plurality of sensors via
the self-configuring sensor kit network and a second communication
device that transmits sensor kit packets to a backend system via a
public network. The edge device further includes a processing
system having one or more processors that execute
computer-executable instructions that cause the processing system
to: receive the reporting packets from the communication system;
perform one or more edge operations on the instances of sensor data
in the reporting packets; generate the sensor kit packets based on
the instances of sensor data, wherein each sensor kit packet
includes at least one instance of sensor data; and output the
sensor kits packets to the communication system, wherein the
communication system transmits the reporting packets to the backend
system via the public network.
[0122] In some embodiments, the sensor kit further includes a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device. In some of these embodiments, the
gateway device includes a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network. Alternatively, in
some embodiments, the gateway device includes a cellular chipset
that is pre-configured to transmit sensor kit packets to a
cellphone tower of a preselected cellular provider.
[0123] In some embodiments, the second communication device of the
edge device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network.
[0124] In embodiments, the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit.
[0125] In embodiments, the edge device further includes one or more
storage devices that store a model data store that stores one or
more machine-learned models that are each trained to predict or
classify a condition of an industrial component of the underwater
industrial setting and/or the underwater industrial setting based
on a set of features that are derived from instances of sensor data
captured by one or more of the plurality of sensors. In some
embodiments, performing one or more edge operations includes:
generating a feature vector based on one or more instances of
sensor data received from one or more sensors of the plurality of
sensors; inputting the feature vector to the machine-learned model
to obtain a prediction or classification relating to a condition of
a particular industrial component of the underwater industrial
setting or the underwater industrial setting and a degree of
confidence corresponding to the prediction or classification; and
selectively encoding the one or more instances of sensor data prior
to transmission to the backend system based on the condition or
prediction. In embodiments, selectively encoding the one or more
instances of sensor data includes compressing the one or more
instances of sensor data using a lossy codec in response to
obtaining one or more predictions or classifications relating to
conditions of respective industrial components of the underwater
industrial setting and the underwater industrial setting that
collectively indicate that there are likely no issues relating to
any industrial component of the underwater industrial setting and
the underwater industrial setting. In embodiments, compressing the
one or more instances of sensor data using the lossy codec
includes: normalizing the one or more instances of sensor data into
respective pixel values; encoding the respective pixel values into
a video frame; and compressing a block of video frames using the
lossy codec, wherein the lossy codec is a video codec and the block
of video frames includes the video frame and one or more other
video frames that include normalized pixel values of other
instances of sensor data. In embodiments, selectively encoding the
one or more instances of sensor data includes compressing the one
or more instances of sensor data using a lossless codec in response
to obtaining a prediction or classification relating to a condition
of a particular industrial component or the underwater industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the underwater industrial
setting. In embodiments, selectively encoding the one or more
instances of sensor data includes refraining from compressing the
one or more instances of sensor data in response to obtaining a
prediction or classification relating to a condition of a
particular industrial component or the underwater industrial
setting that indicates that there is likely an issue relating to
the particular industrial component or the underwater industrial
setting. In embodiments, performing one or more edge operations
includes: generating a feature vector based on one or more
instances of sensor data received from one or more sensors of the
plurality of sensors; inputting the feature vector to the
machine-learned model to obtain a prediction or classification
relating to a condition of a particular industrial component of the
underwater industrial setting or the underwater industrial setting
and a degree of confidence corresponding to the prediction or
classification; and selectively storing the one or more instances
of sensor data in a storage device of the edge device based on the
prediction or classification. In embodiments, selectively storing
the one or more instances of sensor data includes storing the one
or more instances of sensor data in the storage device with an
expiry in response to obtaining one or more predictions or
classifications relating to conditions of respective industrial
components of the underwater industrial setting and the underwater
industrial setting that collectively indicate that there are likely
no issues relating to any industrial component of the underwater
industrial setting and the underwater industrial setting, such that
the one or more instances of sensor data are purged from the
storage device in accordance with the expiry. In embodiments,
selectively storing the one or more instances of sensor data
includes storing the one or more instances of sensor data in the
storage device indefinitely in response to obtaining a prediction
or classification relating to a condition of a particular
industrial component or the underwater industrial setting that
indicates that there is likely an issue relating to the particular
industrial component or the underwater industrial setting.
[0126] In embodiments, the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In some of
these embodiments, the computer-executable instructions further
cause the one or more processors of the edge device to initiate
configuration of the self-configuring sensor kit network.
[0127] In embodiments, the self-configuring sensor kit network is a
mesh network such that: the communication device of each sensor of
the plurality of sensors is configured to establish a communication
channel with at least one other sensor of the plurality of sensors;
and at least one sensor of the plurality of sensors is configured
to receive instances of sensor data from one or more other sensors
of the plurality of sensors and to route the received instances of
the sensor data towards the edge device. In some of these
embodiments, the computer-executable instructions further cause the
one or more processors of the edge device to initiate configuration
of the self-configuring sensor kit network, wherein the plurality
of sensors form the mesh network in response to the edge device
initiating configuration of the self-configuring sensor kit
network.
[0128] In some embodiments, the self-configuring sensor kit network
is a hierarchical network. In some of these embodiments, the sensor
kit further includes one or more collection devices configured to
receive reporting packets from one or more sensors of the plurality
of sensors and route the reporting packets to the edge device. In
some of these embodiments, wherein each collection device is
installed in a different respective section of the underwater
industrial setting and collects sensor data from sensors of the
plurality sensors that are deployed in the respective section.
[0129] According to some embodiments of the present disclosure, a
system for monitoring an industrial setting is disclosed. The
system includes a set of sensor kits each having a set of sensors
that are registered to respective industrial settings and
configured to monitor physical characteristics of the industrial
settings. The system also includes a set of communication gateway
for communicating instances of sensor values from the sensor kits
to a backend system. The backend system is configured to process
the instances of sensor values to monitor the industrial setting,
wherein upon receiving registration data for a sensor kit to an
industrial setting, the backend system automatically configures and
populates a dashboard for an owner or operator of the industrial
setting. The dashboard provides monitoring information that is
based on the instances of sensor values for the industrial
setting.
[0130] In embodiments, the registration of the sensor kit includes
an interface for specifying a type of entity or industrial setting
to be monitored. In some of these embodiments, the backend system
configures the dashboard based on the registered type of entity or
industrial setting. In embodiments, the backend system includes an
analytics facility that is configured based on the type of entity
or industrial setting. In embodiments, the backend system includes
a machine learning facility that is configured based on the type of
entity or industrial setting.
[0131] In embodiments, the communication gateway is configured to
provide a virtual container for instances of sensor values such
that only a registered owner or operator of the industrial setting
can access the sensor values.
[0132] In embodiments, upon registration of a sensor kit to an
industrial setting, a user may select a set or parameters for
monitoring and wherein a set of services and capabilities of the
backend system is automatically provisioned based on the selected
parameters.
[0133] In embodiments, at least one of the sensor kit, the
communication gateway and the backend system includes an edge
computation system for automatically calculating a metric for an
industrial setting based on a plurality of instances of sensor
values from a set of sensor kits.
[0134] In embodiments, the sensor kit is a self-configuring sensor
kit network. In some embodiments, the sensor kit network is a star
network such that each sensor of the plurality of sensors transmits
respective instances of sensor data with the communication gateway
directly using a short-range communication protocol. In some
embodiments, computer-executable instructions cause one or more
processors of the communication gateway device to initiate
configuration of the self-configuring sensor kit network. In some
embodiments, the self-configuring sensor kit network is a mesh
network such that: a communication device of each sensor of the
plurality of sensors is configured to establish a communication
channel with at least one other sensor of the plurality of sensors;
and at least one sensor of the plurality of sensors is configured
to receive instances of sensor data from one or more other sensors
of the plurality of sensors and to route the received instances of
the sensor data towards the communication gateway. In some
embodiments, the computer-executable instructions further cause the
one or more processors of the communication gateway to initiate
configuration of the self-configuring sensor kit network, wherein
the plurality of sensors form the mesh network in response to the
communication gateway initiating configuration of the
self-configuring sensor kit network. In some embodiments, the
self-configuring sensor kit network is a hierarchical network.
[0135] According to some embodiments of the present disclosure, a
system for monitoring an industrial setting is disclosed. The
system includes: a set of sensor kits each having a set of sensors
that are registered to respective industrial settings and
configured to monitor physical characteristics of the industrial
settings; a set of communication gateways for communicating
instances of sensor values from the sensor kits to a backend
system; and said backend system for processing the instances of
sensor values to monitor the industrial setting, wherein upon
receiving registration data for a sensor kit to an industrial
setting, the backend system automatically configures and populates
a dashboard for an owner or operator of the industrial setting,
wherein the dashboard provides monitoring information that is based
on the instances of sensor values for the industrial setting. In
some embodiments, the registration of the sensor kit includes an
interface for specifying a type of entity or industrial setting to
be monitored. In embodiments, the backend system configures the
dashboard based on the registered type of entity or industrial
setting. In some embodiments, the backend system includes an
analytics facility that is configured based on the type of entity
or industrial setting. In embodiments, the backend system includes
a machine learning facility that is configured based on the type of
entity or industrial setting.
[0136] In some embodiments, the communication gateway is configured
to provide a virtual container for instances of sensor values such
that only a registered owner or operator of the industrial setting
can access the sensor values. In embodiments, upon registration of
a sensor kit to an industrial setting, a user may select a set of
parameters for monitoring and wherein a set of services and
capabilities of the backend system is automatically provisioned
based on the selected parameters. In some embodiments, at least one
of the sensor kit, the communication gateway and the backend system
includes an edge computation system for automatically calculating a
metric for an industrial setting based on a plurality of instances
of sensor values from a set of sensor kits.
[0137] In some embodiments, the sensor kit is a self-configuring
sensor kit network. In some of these embodiments, the sensor kit
network is a star network such that each sensor of the plurality of
sensors transmits respective instances of sensor data with the
communication gateway directly using a short-range communication
protocol. In embodiments, computer-executable instructions cause
one or more processors of the communication gateway device to
initiate configuration of the self-configuring sensor kit
network.
[0138] In some embodiments, the self-configuring sensor kit network
is a mesh network such that: a communication device of each sensor
of the plurality of sensors is configured to establish a
communication channel with at least one other sensor of the
plurality of sensors; and at least one sensor of the plurality of
sensors is configured to receive instances of sensor data from one
or more other sensors of the plurality of sensors and to route the
received instances of the sensor data towards the communication
gateway. In some of these embodiments, the computer-executable
instructions further cause the one or more processors of the
communication gateway to initiate configuration of the
self-configuring sensor kit network, wherein the plurality of
sensors form the mesh network in response to the communication
gateway initiating configuration of the self-configuring sensor kit
network. In some embodiments, the self-configuring sensor kit
network is a hierarchical network.
[0139] According to some embodiments of the present disclosure, a
method of monitoring a plurality of industrial settings using a set
of sensors kits, a set of communication gateways, and a backend
system is disclosed. The method includes: registering each sensor
kit of the plurality of sensor kits to a respective industrial
setting of the plurality of industrial settings; configuring each
sensor kit of the plurality of sensor kits to monitor physical
characteristics of the respective industrial setting to which the
sensor kit is registered; transmitting, by each communication
gateway of the set of communication gateways, instances of sensor
data from a respective sensor kit of the plurality of sensor kits
to the backend system; processing, by the backend system, the
instances of sensor data received from each sensor kit of the
plurality of sensor kits; automatically configuring and populating,
by the backend system, a dashboard for an owner or operator of the
respective industrial setting upon receiving registration data for
a sensor kit of the plurality of sensor kits; and providing, by the
dashboard, monitoring information that is based on the instances of
sensor data for the respective industrial setting.
[0140] In some embodiments, registering each sensor kit includes
providing an interface for specifying a type of entity or
industrial setting to be monitored. In some of these embodiments,
configuring each sensor kit to monitor physical characteristics of
the respective industrial setting includes configuring, by the
backend system, the dashboard based on the registered type of
entity or industrial setting. In some embodiments, the backend
system includes an analytics facility that is configured based on
the type of entity of the industrial setting. In embodiments, the
backend system includes a machine learning facility that is
configured based on the type of entity or industrial setting.
[0141] In some embodiments, the method further includes providing,
by each communication gateway of the plurality of communication
gateways, a virtual container for instances of sensor data such
that only a registered owner or operator of the respective
industrial setting can access the sensor data. In embodiments, upon
registration of a sensor kit to an industrial setting, a user may
select a set of parameters for monitoring. In some embodiments, the
method further includes automatically provisioning, by the backend
system, a set of services and capabilities of the backend system
based on the selected parameters. In embodiments, at least one of a
sensor kit of the plurality of sensor kits, a communication gateway
of the plurality of communication gateways, and the backend system
includes an edge computation system for automatically calculating a
metric for an industrial setting based on a plurality of instances
of sensor data from a set of sensor kits.
[0142] In some embodiments, at least one sensor kit of the
plurality of sensor kits is a self-configuring sensor kit network
including a plurality of sensors. In some of these embodiments, the
method further includes: capturing, by the plurality of sensors,
sensor data; and transmitting, by the plurality of sensors, the
sensor data to and edge device via the self-configuring sensor kit
network. In some embodiments, transmitting the sensor data via the
self-configuring sensor kit network includes directly transmitting,
by each sensor of the plurality of sensors, instances of sensor
data with the edge device using a short-range communication
protocol, wherein the self-configuring sensor kit network is a star
network. In some embodiments, the method further includes
initiating, by the edge processing system, configuration of the
self-configuring sensor kit network.
[0143] In embodiments, the self-configuring sensor kit network is a
mesh network and each sensor of the plurality of sensors includes a
communication device. In some of these embodiments, the method
further includes: establishing, by the communication device of each
sensor of the plurality of sensors, a communication channel with at
least one other sensor of the plurality of sensors; receiving, by
at least one sensor of the plurality of sensors, instances of
sensor data from one or more other sensors of the plurality of
sensors; and routing, by the at least one sensor of the plurality
of sensors, the received instances of the sensor data towards the
edge device.
[0144] In some embodiments, the self-configuring sensor kit network
is a hierarchical network and the sensor kit includes one or more
collection devices. In some embodiments, the plurality of sensors
includes a first set of sensors of a first sensor type and a second
set of sensors of a second sensor type.
[0145] According to some embodiments of the present disclosure, a
sensor kit configured for monitoring an industrial setting is
disclosed. The sensor kit includes: an edge device; and a plurality
of sensors that capture sensor data and transmit the sensor data
via a self-configuring sensor kit network, wherein the plurality of
sensors includes one or more sensors of a first sensor type and one
or more sensors of a second sensor type, wherein at least one
sensor of the plurality of sensors includes: a sensing component
that captures sensor measurements and outputs instances of sensor
data; a processing unit that generates reporting packets based on
one or more instances of sensor data and outputs the reporting
packets, wherein each reporting packet includes routing data and
one or more instances of sensor data; and a communication device
configured to receive reporting packets from the processing unit
and to transmit the reporting packets to the edge device via the
self-configuring sensor kit network in accordance with a first
communication protocol. The edge device includes: a communication
system having: a first communication device that receives reporting
packets from the plurality of sensors via the self-configuring
sensor kit network; and a second communication device that
transmits sensor kit packets to a backend system via a public
network; a processing system having one or more processors that
execute computer-executable instructions that cause the processing
system to: receive the reporting packets from the communication
system; generate a data block based on sensor data obtained from
the reporting packets, wherein the data block includes (i) a block
header that defines an address of the data block and (ii) a block
body that defines the sensor data and a parent address of another
data block to which the data block will be linked; and transmit the
data block to one or more node computing devices that collectively
store a distributed ledger that is comprised of a plurality of data
blocks.
[0146] In some embodiments, generating the data block includes
generating a hash value of the block body. In embodiments,
generating the data block includes encrypting the block body.
[0147] In some embodiments, the distributed ledger includes a smart
contract that defines one or more conditions relating to collected
sensor data and one or more actions that are initiated by the smart
contract in response to the one or more conditions being satisfied.
In some embodiments, the smart contract receives the data block
from the sensor kit and determines whether the one or more
conditions are satisfied based on at least the sensor data stored
in the data block. In embodiments, the smart contract corresponds
to an insurer. In some embodiments, the action defined in the smart
contract triggers a transfer of funds to an account associated with
an operator associated with the sensor kit in response to
satisfying the one or more conditions. In embodiments, the one or
more conditions include a first condition that determines whether
the sensor kit has reported a sufficient amount of sensor data and
a second condition that determines whether the reported sensor data
indicates that the industrial setting is operating without
issue.
[0148] In some embodiments, the smart contract corresponds to a
regulatory body. In some of these embodiments, the action defined
in the smart contract triggers an issuance of a token to an
operator associated with the sensor kit in response to satisfying
the one or more conditions. In embodiments, the one or more
conditions include a first condition that requires a certain amount
of reported sensor data to be reported by a sensor kit and a second
condition that requires the reported sensor data to be compliant
with the reporting regulations.
[0149] In some embodiments, the edge device is one of the node
computing devices.
[0150] According to some embodiments of the present disclosure, a
method for monitoring an industrial setting using a sensor kit
having a plurality of sensors and an edge device including a
processing system is disclosed. The method includes: receiving, by
the processing system, reporting packets from one or more
respective sensors of the plurality of sensors, wherein each
reporting packet includes routing data and one or more instances of
sensor data; generating, by the processing system, a data block
based on sensor data obtained from the reporting packets, wherein
the data block includes (i) a block header that defines an address
of the data block and (ii) a block body that defines the sensor
data and a parent address of another data block to which the data
block will be linked; and transmitting, by the processing system,
the data block to one or more node computing devices that
collectively store a distributed ledger that is comprised of a
plurality of data blocks. In some embodiments, generating the data
block includes generating, by the processing system, a hash value
of the block body. In embodiments, generating the data block
includes encrypting, by the processing system, the block body.
[0151] In some embodiments, the distributed ledger includes a smart
contract that defines one or more conditions relating to collected
sensor data and one or more actions that are initiated by the smart
contract in response to the one or more conditions being satisfied.
In some of these embodiments, the smart contract receives the data
block from the sensor kit and determines whether the one or more
conditions are satisfied based on at least the sensor data stored
in the data block. In some embodiments, the smart contract
corresponds to an insurer. In embodiments, the action defined in
the smart contract triggers a transfer of funds to an account
associated with an operator associated with the sensor kit in
response to satisfying the one or more conditions. In some
embodiments, the one or more conditions include a first condition
that determines whether the sensor kit has reported a sufficient
amount of sensor data and a second condition that determines
whether the reported sensor data indicates that the industrial
setting is operating without issue.
[0152] In some embodiments, the smart contract corresponds to a
regulatory body. In some of these embodiments, the action defined
in the smart contract triggers an issuance of a token to an
operator associated with the sensor kit in response to satisfying
the one or more conditions.
[0153] In some embodiments, the one or more conditions include a
first condition that requires a certain amount of reported sensor
data to be reported by a sensor kit and a second condition that
requires the reported sensor data to be compliant with the
reporting regulations.
[0154] In some embodiments, the edge device is one of the node
computing devices.
[0155] In some embodiments, the plurality of sensors includes a
first set of sensors of a first sensor type and a second set of
sensors of a second sensor type.
[0156] According to some embodiments of the present disclosure, a
system is disclosed. The system includes: a backend system
including one or more servers configured to deploy a smart contract
to a distributed ledger on behalf of a user, wherein the smart
contract defines one or more conditions relating to collected
sensor data and one or more actions that are initiated by the smart
contract in response to the one or more conditions being satisfied;
a sensor kit configured for monitoring an industrial setting, the
sensor kit including: an edge device; and a plurality of sensors
that capture sensor data and transmit the sensor data via a
self-configuring sensor kit network, wherein the plurality of
sensors includes one or more sensors of a first sensor type and one
or more sensors of a second sensor type, wherein at least one
sensor of the plurality of sensors includes: a sensing component
that captures sensor measurements and outputs instances of sensor
data; a processing unit that generates reporting packets based on
one or more instances of sensor data and outputs the reporting
packets, wherein each reporting packet includes routing data and
one or more instances of sensor data; and a communication device
configured to receive reporting packets from the processing unit
and to transmit the reporting packets to the edge device via the
self-configuring sensor kit network in accordance with a first
communication protocol. The edge device includes: a communication
system having a first communication device that receives reporting
packets from the plurality of sensors via the self-configuring
sensor kit network, and a second communication device that
transmits sensor kit packets to a backend system via a public
network; a processing system having one or more processors that
execute computer-executable instructions that cause the processing
system to: receive the reporting packets from the communication
system; generate a data block based on sensor data obtained from
the reporting packets, wherein the data block includes (i) a block
header that defines an address of the data block and (ii) a block
body that defines the sensor data and a parent address of another
data block to which the data block will be linked; and transmit the
data block to one or more node computing devices that collectively
store a distributed ledger that is comprised of a plurality of data
blocks.
[0157] In some embodiments, generating the data block includes
generating a hash value of the block body. In some embodiments,
generating the data block includes encrypting the block body.
[0158] In some embodiments, the smart contract receives the data
block from the sensor kit and determines whether the one or more
conditions are satisfied based on at least the sensor data stored
in the data block. In some of these embodiments, the smart contract
corresponds to an insurer. In some embodiments, the action defined
in the smart contract triggers a transfer of funds to an account
associated with an operator associated with the sensor kit in
response to satisfying the one or more conditions. In embodiments,
the one or more conditions include a first condition that
determines whether the sensor kit has reported a sufficient amount
of sensor data and a second condition that determines whether the
reported sensor data indicates that the industrial setting is
operating without issue. In some embodiments, the smart contract
corresponds to a regulatory body. In embodiments, the action
defined in the smart contract triggers an issuance of a token to an
operator associated with the sensor kit in response to satisfying
the one or more conditions. In some embodiments, the one or more
conditions include a condition that determines whether the sensor
kit has reported a required amount of sensor data as defined by a
regulation.
[0159] In some embodiments, the edge device is one of the node
computing devices.
[0160] According to some embodiments of the present disclosure, a
method for monitoring an industrial setting using a sensor kit in
communication with a backend system, the sensor kit including a
plurality of sensors and an edge device, is disclosed. The method
includes: deploying, by the backend system, a smart contract to a
distributed ledger on behalf of a user, wherein the smart contract
defines one or more conditions relating to collected sensor data
and one or more actions that are initiated by the smart contract in
response to the one or more conditions being satisfied; receiving,
by an edge processing system of the edge device, reporting packets
from one or more respective sensors of the plurality of sensors,
wherein each reporting packet includes routing data and one or more
instances of sensor data; generating, by the edge processing
system, a data block based on sensor data obtained from the
reporting packets, wherein the data block includes (i) a block
header that defines an address of the data block and (ii) a block
body that defines the sensor data and a parent address of another
data block to which the data block will be linked; and
transmitting, by the edge processing system, the data block to one
or more node computing devices that collectively store a
distributed ledger that is comprised of a plurality of data
blocks.
[0161] In some embodiments, generating the data block includes
generating, by the edge processing system, a hash value of the
block body. In embodiments, generating the data block includes
encrypting, by the edge processing system, the block body.
[0162] In some embodiments, the distributed ledger receives the
data block from the sensor kit and determines whether the one or
more conditions of the smart contract are satisfied based on at
least the sensor data stored in the data block. In some of these
embodiments, the smart contract corresponds to an insurer. In
embodiments, the action defined in the smart contract triggers a
transfer of funds to an account associated with an operator
associated with the sensor kit in response to satisfying the one or
more conditions. In some embodiments, the one or more conditions
include a first condition that determines whether the sensor kit
has reported a sufficient amount of sensor data and a second
condition that determines whether the reported sensor data
indicates that the industrial setting is operating without
issue.
[0163] In some embodiments, the smart contract corresponds to a
regulatory body. In some of these embodiments, the action defined
in the smart contract triggers an issuance of a token to an
operator associated with the sensor kit in response to satisfying
the one or more conditions. In some embodiments, the one or more
conditions include a condition that determines whether the sensor
kit has reported a required amount of sensor data as defined by a
regulation. In embodiments, the edge device is one of the node
computing devices. In some embodiments, the backend system is one
of the node computing devices. In embodiments, the plurality of
sensors includes a first set of sensors of a first sensor type and
a second set of sensors of a second sensor type.
[0164] A more complete understanding of the disclosure will be
appreciated from the description and accompanying drawings and the
claims, which follow.
BRIEF DESCRIPTION OF THE DRAWINGS
[0165] The accompanying drawings, which are included to provide a
better understanding of the disclosure, illustrate embodiment(s) of
the disclosure and together with the description serve to explain
the principle of the disclosure. In the drawings:
[0166] FIG. 1 is a schematic illustrating an example of a sensor
kit deployed in an industrial setting according to some embodiments
of the present disclosure.
[0167] FIG. 2A is a schematic illustrating an example of a sensor
kit network having a star network topology according to some
embodiments of the present disclosure.
[0168] FIG. 2B is a schematic illustrating an example of a sensor
kit network having a mesh network topology according to some
embodiments of the present disclosure.
[0169] FIG. 2C is a schematic illustrating an example of a sensor
kit network having a hierarchical network topology according to
some embodiments of the present disclosure.
[0170] FIG. 3A is a schematic illustrating an example of a sensor
according to some embodiments of the present disclosure.
[0171] FIG. 3B is a schematic illustrating an example schema of a
reporting packet according to some embodiments of the present
disclosure.
[0172] FIG. 4 is a schematic illustrating an example of an edge
device of a sensor kit according to some embodiments of the present
disclosure.
[0173] FIG. 5 is a schematic illustrating an example of a backend
system that receives sensor data from sensor kits deployed in
industrial settings according to some embodiments of the present
disclosure.
[0174] FIG. 6 is a flow chart illustrating an example set of
operations of a method for encoding sensor data captured by a
sensor kit according to some embodiments of the present
disclosure.
[0175] FIG. 7 is a flow chart illustrating an example set of
operations of a method for decoding sensor data provided to a
backend system by a sensor kit according to some embodiments of the
present disclosure.
[0176] FIG. 8 is a flow chart illustrating an example set of
operations of a method for encoding sensor data captured by a
sensor kit using a media codec according to some embodiments of the
present disclosure.
[0177] FIG. 9 is a flow chart illustrating an example set of
operations of a method for decoding sensor data provided to a
backend system by a sensor kit using a media codec according to
some embodiments of the present disclosure.
[0178] FIG. 10 is a flow chart illustrating an example set of
operations of a method for determining a transmission strategy
and/or a storage strategy for sensor data collected by a sensor kit
based on the sensor data, according to some embodiments of the
present disclosure
[0179] FIGS. 11-15 are schematics illustrating different
configurations of sensor kits according to some embodiments of the
present disclosure.
[0180] FIG. 16 is a flowchart illustrating an example set of
operations of a method for monitoring industrial settings using an
automatically configured backend system, according to some
embodiments of the present disclosure.
[0181] FIG. 17 is a plan view of a manufacturing facility
illustrating an exemplary implementation of a sensor kit including
an edge device, according to some embodiments of the present
disclosure.
[0182] FIG. 18 is a plan view of a surface portion of an underwater
industrial facility illustrating an exemplary implementation of a
sensor kit including an edge device, according to some embodiments
of the present disclosure.
[0183] FIG. 19 is a plan view of an indoor agricultural facility
illustrating an exemplary implementation of a sensor kit including
an edge device, according to some embodiments of the present
disclosure.
DETAILED DESCRIPTION
[0184] Various configurations of sensor kits are disclosed. A
sensor kit may be a purpose-configured system that includes sensors
for monitoring a specific type of industrial setting, wherein the
sensors are provided in a unified kit, optionally along with other
devices, systems and components, such as ones that provide
communication, processing and intelligence capabilities. In
embodiments, an owner or operator of an industrial setting may
purchase or otherwise obtain the sensor kit. During the purchase
process, the owner or operator, or a user associated with the
industrial setting, may provide or indicate one or more features of
the industrial setting (e.g., type of the setting, location of the
setting, size of the setting, whether the setting is indoors or
outdoors, the components and/or types of components being
monitored, the number of each component and/or type of component
being monitored, and the like). In embodiments, the sensor kit may
be preconfigured based on features and requirements of the
industrial operator or owner. The sensor kit may be preconfigured
such that the owner or operator may install the sensor kit in a
"plug-and-play" manner, whereby the owner or operator does not need
to configure a sensor kit network on which the devices of the
sensor kit communicate.
[0185] FIG. 1--Sensor Kit Environment
[0186] FIG. 1 is a schematic illustrating an industrial setting 120
at which a sensor kit 100 has been installed. In embodiments, the
sensor kit 100 may refer to a fully deployable, purpose-configured
industrial IoT system that is provided in a unified kit and is
ready for deployment in the industrial setting 120 by a consumer
entity (e.g., owner or operator of an industrial setting 120). In
embodiments, the sensor kit 100 allows the owner or operator to
install and deploy the sensor kit with no or minimal configuration
(e.g., setting user permissions, setting passwords, and/or setting
notification and/or display preferences). The term "sensor kit" 100
may refer to a set of devices that are installed in an industrial
setting 120 (e.g., a factory, a mine, an oil field, an oil
pipeline, a refinery, a commercial kitchen, an industrial complex,
a storage facility, a building site, and the like). The collection
of devices comprising the sensor kit 100 includes a set of one or
more internet of things (IoT) sensors 102 and a set of one or more
edge devices 104. For purposes of discussion, references to
"sensors" or "sensor devices" should be understood to mean IoT
sensors, unless specifically stated otherwise.
[0187] In embodiments, the sensor kit 100 includes a set of IoT
sensors 102 that are configured for deployment in, on, or around an
industrial component, a type of an industrial component (e.g., a
turbine, a generator, a fan, a pump, a valve, an assembly line, a
pipe or pipeline, a food inspection line, a server rack, and the
like), an industrial setting 120, and/or a type of industrial
setting 120 (e.g., indoor, outdoor, manufacturing, mining,
drilling, resource extraction, underground, underwater, and the
like) and a set of edge devices capable of handling inputs from the
sensors and providing network-based communications. In embodiments,
an edge device 104 may include or may communicate with a local data
processing system (e.g., a device configured to compress sensor
data, filter sensor data, analyze sensor data, issue notifications
based on sensor data and the like) capable of providing local
outputs, such as of signals and of analytic results that result
from local processing. In embodiments, the edge device 104 may
include or may communicate with a communication system (e.g., a
Wi-Fi chipset, a cellular chipset, a satellite transceiver,
cognitive radio, one or more Bluetooth chips and/or other
networking device) that is capable of communicating data (e.g., raw
and/or processed sensor data, notifications, command instructions,
etc.) within and outside the industrial environment. In
embodiments, the communication system is configured to operate
without reliance on the main data or communication networks of an
industrial setting 120. In embodiments, the communication system is
provided with security capabilities and instructions that maintain
complete physical and data separation from the main data or
communication networks of an industrial setting 120. For example,
in embodiments, Bluetooth-enabled edge devices may be configured to
permit pairing only with pre-registered components of a kit, rather
than with other Bluetooth-enabled devices in an industrial setting
120.
[0188] In embodiments, an IoT sensor 102 is a sensor device that is
configured to collect sensor data and to communicate sensor data to
another device using at least one communication protocol. In
embodiments, IoT sensors 102 are configured for deployment in, on,
or around a defined type of an industrial entity. The term
industrial entity may refer to any object that may be monitored in
an industrial setting 120. In embodiments, industrial entities may
include industrial components (e.g., a turbine, a generator, a fan,
a pump, a valve, an assembly line, a pipe or pipe line, a food
inspection line, a server rack, and the like). In embodiments,
industrial entities may include organisms that are associated with
an industrial setting 120 (e.g., humans working in the industrial
setting 120 or livestock being monitored in the industrial setting
120). Depending on the intended use, setting, or purpose of the
sensor kit 100, the configuration and form factor of an IoT sensor
102 will vary. Examples of different types of sensors include:
vibration sensors, inertial sensors, temperature sensors, humidity
sensors, motion sensors, LIDAR sensors, smoke/fire sensors, current
sensors, pressure sensors, pH sensors, light sensors, radiation
sensors, and the like.
[0189] In embodiments, an edge device 104 may be a computing device
configured to receive sensor data from the one or more IoT sensors
102 and perform one or more edge-related processes relating to the
sensor data. An edge-related process may refer to a process that is
performed at an edge device 104 in order to store the sensor data,
reduce bandwidth on a communication network, and/or reduce the
computational resources required at a backend system. Examples of
edge processes can include data filtering, signal filtering, data
processing, compression, encoding, quick-predictions,
quick-notifications, emergency alarming, and the like.
[0190] In embodiments, a sensor kit 100 is pre-configured such that
the devices (e.g., sensors 102, edge devices 104, collection
devices, gateways, etc.) within the sensor kit 100 are configured
to communicate with one another via a sensor kit network without a
user having to configure the sensor kit network. A sensor kit
network may refer to a closed communication network that is
established between the various devices of the sensor kit and that
utilizes two or more different communication protocols and/or
communication mediums to enable communication of data between the
devices and to a broader communication network, such as a public
communication network 190 (e.g., the Internet, a satellite network,
and/or one or more cellular networks). For example, while some
devices in a sensor kit network may communicate using a Bluetooth
communication protocol, other devices may communicate with one
another using a near-field communication protocol, a Zigbee
protocol, and/or a Wi-Fi communication protocol. In some
implementations, a sensor kit 100 may be configured to establish a
mesh network having various devices acting as routing nodes within
the sensor kit network. For example, sensors 102 may be configured
to collect data and transmit the collected data to the edge device
104 via the sensor kit network, but may also be configured to
receive and route data packets from other sensors 102 within the
sensor kit network towards an edge device 104.
[0191] In embodiments, a sensor kit network may include additional
types of devices. In embodiments, a sensor kit 100 may include one
or more collection devices (not shown in FIG. 1) that act as
routing nodes in the sensor network, such that the collection
devices may be part of a mesh network. In embodiments, a sensor kit
100 may include a gateway device (not shown in FIG. 1) that enable
communication with a broader network, whereby the gateway device
may communicate with the edge device 104 over a wired or wireless
communication medium in industrial settings 120 that would prevent
an edge device 104 from communicating with the public communication
network 190 (e.g., in a factory having very thick concrete walls).
Embodiments of the sensor kit 100 may include additional devices
without departing from the scope of the disclosure.
[0192] In embodiments, the sensor kit 100 is configured to
communicate with a backend system 150 via a communication network,
such as the public communication network 190. In embodiments, the
backend system 150 is configured to receive sensor data from a
sensor kit 100 and to perform one or more backend operations on the
received sensor data. Examples of backend operations may include
storing the sensor data in a database, performing analytics tasks
on the sensor data, providing the results of the analytics and/or
visualizations of the sensor data to a user via a portal and/or a
dashboard, training one or more machine-learned models using the
sensor data, determining predictions and/or classifications
relating to the operation of the industrial setting 120 and/or
industrial devices of the industrial setting 120 based on the
sensor data, controlling an aspect and/or an industrial device of
the industrial setting 120 based on the predictions and/or
classifications, issuing notifications to the user via the portal
and/or the dashboard based on the predictions and/or
classifications, and the like.
[0193] It is appreciated that in some embodiments, the sensor kit
100 may provide additional types of data to the backend system 150.
For example, the sensor kit 100 may provide diagnostic data
indicating any detected issues (e.g., malfunction, battery levels
low, etc.) or potential issues with the sensors 102 or other
devices in the sensor kit 100.
[0194] In embodiments, the sensor kit 100 is configured to
self-monitor for failing components (e.g., failing sensors 102) and
to report failing components to the operator. For example, in some
embodiments, the edge device 104 may be configured to detect
failure of a sensor 102 based on a lack of reporting from a sensor,
a lack of response to requests (e.g., "pings"), and/or based on
unreliable data (e.g., data regularly falling out of the expected
sensor readings). In some embodiments, the edge device 104 can
maintain a sensor kit network map indicating where each device in
the sensor kit network is located and can provide approximate
locations and/or identifiers of failed sensors to a user.
[0195] In embodiments, the sensor kit 100 may be implemented to
allow post-installation configuration. A post-installation
configuration may refer to an update to the sensor kit 100 by
adding devices and/or services to the sensor kit 100 after the
sensor kit 100 has been installed. In some of these embodiments,
users (e.g., operators of the industrial setting 120) of the system
may subscribe to or purchase certain edge "services." For example,
the sensor kit 100 may be configured to execute certain programs
installed on one or more devices of the sensor kit 100 only if the
user has a valid subscription or ownership permission to access the
edge service supported by the program. When the user no longer has
the valid subscription and/or ownership permission, the sensor kit
100 may preclude execution of those programs. For example, a user
may subscribe to unlock AI-based edge services, mesh networking
capabilities, self-monitoring services, compression services,
in-facility notifications, and the like.
[0196] In some embodiments, users can add new sensors 102 to the
sensor kit post-installation in a plug-and-play-like manner. In
some of these embodiments, the edge device 104 and the sensors 102
(or other devices to be added to the sensor kit 100) may include
respective short-range communication capabilities (e.g., near-field
communication (NFC) chips, RFID chips, Bluetooth chips, Wi-Fi
adapters, and the like). In these embodiments, the sensors 102 may
include persistent storage that stores identifying data (e.g., a
sensor identifier value) and any other data that would be used to
add the sensor 102 to the sensor kit 100 (e.g., an industrial
device type, supported communication protocols, and the like). In
some embodiments, a user may initiate a post-installation addition
to the sensor kit 100 by pressing a button on the edge device 104,
and/or by bringing the sensor 102 into the vicinity of the edge
device 104. In some embodiments, in response to a user initiating a
post-installation addition to the sensor kit, the edge device 104
may emit a signal (e.g., a radio frequency). The edge device 104
may emit the signal, for example, as a result of a human user
pushing a button or at a predetermined time interval. The emitted
signal may trigger a sensor 102 proximate enough to receive the
signal and to transmit the sensor ID of the sensor 102 and any
other suitable configuration data (e.g., device type, communication
protocols, and the like). In response to the sensor 102
transmitting its configuration data (e.g., sensor ID and other
relevant configuration data) to the edge device 104, the edge
device 104 may add the sensor 102 to the sensor kit 102. Adding the
sensor 102 to the sensor kit 104 may include updating a data store
or manifest stored at the edge device 104 that identifies the
devices of the sensor kit 100 and data relating thereto.
Non-limiting examples of data that may be stored in the manifest
relating to each respective sensor 102 may include the
communication protocol used by the sensor 102 to communicate with
the edge device 104 (or intermediate devices), the type of sensor
data provided by the sensor 102 (e.g., vibration sensor data,
temperature data, humidity data, etc.), models used to analyze
sensor data from the sensor 102 (e.g., a model identifier), alarm
limits associated with the sensor 102, and the like.
[0197] In embodiments, the sensor kit 100 (e.g., the edge device
104) may be configured to update a distributed ledger 162 with
sensor data captured by the sensor kit 100. In embodiments, a
distributed ledger 162 is a Blockchain or any other suitable
distributed ledger 162. The distributed ledger 162 may be a public
ledger or a private ledger. Private ledgers reduce power
consumption requirements of maintaining the distributed ledger 162,
while public ledgers consume more power but offer more robust
security. In embodiments, the distributed ledger 162 may be
distributed amongst a plurality of node computing devices 160. The
node computing devices 160 may be any suitable computing device,
including physical servers, virtual servers, personal computing
devices, and the like. In some embodiments, the node computing
devices 160 are approved (e.g., via a consensus mechanism) before
the node computing devices 160 may participate in the distributed
ledger. In some embodiments, the distributed ledger 162 may be
privately stored. For example, a distributed ledger may be stored
amongst a set of preapproved node computing devices, such that the
distributed ledger 162 is not accessible by non-approved devices.
In some embodiments, the node computing devices 160 are edge
devices 104 of the sensor kit 102 and other sensor kits 102.
[0198] In embodiments, the distributed ledger 162 is comprised of a
set of linked data structures (e.g., blocks, data records, etc.),
such that the linked data structures form an acyclic graph. For
purposes of explanation, the data structures will be referred to as
blocks. In embodiments, each block may include a header that
includes a unique ID of the block and a body that includes the data
that is stored in the block, and a pointer. In embodiments, the
pointer is the block ID of a parent block of the block, wherein the
parent block is a block that was created prior to the block being
written. The data stored in a respective block can be sensor data
captured by a respective sensor kit 100. Depending on the
implementation, the types of sensor data and the amount of sensor
data stored in a respective body of a block may vary. For example,
a block may store a set of sensor measurements from one or more
types of sensors 102 of the sensor kit 100 captured over a period
of time (e.g., sensor data 102 captured from all of the sensors 102
in the sensor kit 100 over a period one hour or one day) and
metadata relating thereto (e.g., sensor identifiers of each sensor
measurement and a timestamp of each sensor measurement or group of
sensor measurements). In some embodiments, a block may store sensor
measurements determined to be anomalous (e.g., outside a standard
deviation of expected sensor measurements or deltas in sensor
measurements that are above a threshold) and/or sensor measurements
indicative of an issue or potential issue, and related metadata
(e.g., sensor IDs of each sensor measurement and a timestamp of
each sensor measurement or group of sensor measurements). In some
embodiments, the sensor data stored in a block may be compressed
and/or encoded sensor data, such that the edge device 104
compresses/encodes the sensor data into a more compact format. In
embodiments, the edge device 104 may generate a hash of the body,
such that the contents of the body (e.g., block ID of the parent
block and the sensor data) are hashed and cannot be altered without
changing the value of the hash. In embodiments, the edge device 104
may encrypt the content within the block, so that the content may
not be read by unauthorized devices.
[0199] As mentioned, the distributed ledger 162 may be used for
different purposes. In some embodiments, the distributed ledger 162
may further include one or more smart contracts. A smart contract
is a self-executing digital contract. A smart contract may include
code (e.g., executable instructions) that defines one or more
conditions that trigger one or more actions. A smart contract may
be written by a developer in a scripting language (e.g.,
JavaScript), an object code language (e.g., Java), or a compiled
language (e.g., C++ or C). Once written, a smart contract may be
encoded in a block and deployed to the distributed ledger 162. In
embodiments, the backend system 150 is configured to receive the
smart contract from a user and write the smart contract to a
respective distributed ledger 162. In embodiments, an address of
the smart contract (e.g., the block ID of the block containing the
smart contract) may be provided to one or more parties to the smart
contract, such that respective parties may invoke the smart
contract using the address. In some embodiments, the smart contract
may include an API that allows a party to provide data (e.g.,
addresses of blocks) and/or to transmit data (e.g., instructions to
transfer funds to an account).
[0200] In example implementations, an insurer may allow insured
owners and/or operators of an industrial setting 120 to agree to
share sensor data with the insurer to demonstrate that the
equipment in the facility is functioning properly and, in return,
the insurer may issue a rebate or refund to the owners and/or
operators if the owners and/or operators are compliant with an
agreement with the insurers. Compliance with the agreement may be
verified electronically by participant nodes in the distributed
ledger and/or the sensor kit 100 via a smart contract. In
embodiments, the insurer may deploy the smart contract (e.g., by
adding the smart contract to a distributed ledger 162) that
triggers the issuance of rebates or refunds on portions of
insurance premiums when the sensor kit 100 provides sufficient
sensor data to the insurer via the distributed ledger that
indicates the facility is operating without issue. In some of these
embodiments, the smart contract may include a first condition that
requires a certain amount of sensor data to be reported by a
facility and a second condition that each instance of the sensor
data equals a value (e.g., there are no classified or predicted
issues) or range of values (e.g., all sensor measurements are
within a predefined range of values). In some embodiments, the
action taken in response to one or more of the conditions being met
may be to deposit funds (e.g., a wire transfer or cryptocurrency)
into an account. In this example, the edge device 104 may write
blocks containing sensor data to the distributed ledger. The edge
device 104 may also provide the addresses of these blocks to the
smart contract (e.g., using an API of the smart contract). Upon the
smart contract verifying the first and second conditions of the
contract, the smart contract may initiate the transfer of funds
from an account of the insurer to the account of the insured.
[0201] In another example, a regulatory body (e.g., a state, local,
or federal regulatory agency) may require facility operators to
report sensor data to ensure compliance with one or more
regulations. For instance, the regulatory body may regulate food
inspection facilities, pharmaceutical manufacturing facilities,
e.g., manufacturing facility 1700, indoor agricultural facilities,
e.g., indoor agricultural facility 1800, offshore oil extraction
facilities, e.g., underwater industrial facility 1900, or the like.
In embodiments, the regulatory body may deploy a smart contract
that is configured to receive and verify the sensor data from an
industrial setting 120, and in response to verifying the sensor
data issues a compliance token (or certificate) to an account of
the facility owner. In some of these embodiments, the smart
contract may include a condition that requires a certain amount of
sensor data to be reported by a facility and a second condition
that requires the sensor data to be compliant with the reporting
regulations. In this example, the edge device 104 may write blocks
containing sensor data to the distributed ledger 162. The edge
device 104 may also provide the addresses of these blocks to the
smart contract (e.g., using an API of the smart contract). Upon the
smart contract verifying the first and second conditions of the
contract, the smart contract may generate a token indicating
compliance by the facility operator and may initiate the transfer
of funds to an account (e.g., a digital wallet) associated with the
facility.
[0202] A distributed ledger 162 may be adapted for additional or
alternative applications without departing from the scope of the
disclosure.
[0203] FIGS. 2A, 2B, and 2C--Components and Networking
[0204] FIGS. 2A, 2B, and 2C illustrate example configurations of a
sensor kit network 200. Depending on the sensor kit 100 and the
industrial setting 120 that the sensor kit 100 is installed in, the
sensor kit network 200 may communicate in different manners.
[0205] FIG. 2A illustrates an example sensor kit network 200A that
is a star network. In these embodiments, the sensors 102
communicate directly with the edge device 104. In these
embodiments, the communication protocol(s) utilized by the sensor
devices 102 and the edge device 104 to communicate are based on one
or more of the physical area of the sensor kit network 102, the
power sources available, and the types of sensors 102 in the sensor
kit 100. For example, in settings where the area being monitored is
a relatively small area and where the sensors 102 are not able to
connect to a power supply, the sensors 102 may be fabricated with a
Bluetooth Low Energy (BLE) microchip that communicates using a
Bluetooth Low Energy protocol (e.g., the Bluetooth 5 protocol
maintained by the Bluetooth Special Interest Group). In another
example, in a relatively small area where lots of sensors 102 are
to be deployed, the sensors 102 may be fabricated with the Wi-Fi
microchip that communicates using the IEEE 802.11 protocol. In the
embodiments of FIG. 2A, the sensors 102 may be configured to
perform one-way or two-way communication. In embodiments where the
edge device 104 does not need to communicate data and/or
instructions to the sensors 102, the sensors 102 may be configured
for one-way communication. In embodiments where the edge device 104
does communicate data and/or instructions to the sensors 102, the
sensors 102 may be configured with transceivers that perform
two-way communication. A star network may be configured with
devices having other suitable communication devices without
departing from the scope of the disclosure.
[0206] FIG. 2B illustrates an example sensor kit network 200B that
is a mesh network where the nodes (e.g., sensors 102) connect to
each other directly, dynamically, and/or non-hierarchically to
cooperate with one another to efficiently route data to and from
the edge device 104. In some embodiments, the devices in the mesh
network (e.g., the sensors 102, the edge device 104, and/or any
other devices in the sensor kit network 200B) may be configured to
self-organize and self-configure the mesh network, such that the
sensors 102 and/or the edge device 104 may determine which devices
route data on behalf of other devices, and/or redundancies for
transmission should a routing node (e.g., sensor 102) fail. In
embodiments, the sensor kit 100 may be configured to implement a
mesh network in industrial settings 120 where the area being
monitored is relatively large (e.g., greater than 100 meters in
radius from the edge device 104) and/or where the sensors 102 in
the sensor kit 100 are intended to be installed in close proximity
to one another. In the latter scenario, the power consumption of
each individual sensor 102 may be reduced in comparison to sensors
102 in a star network, as the distance that each respective sensor
102 needs to transmit over is relatively less than the distance
that the respective sensor 102 would need to transmit over in a
star network. In embodiments, a sensor 102 may be fabricated with a
Zigbee.RTM. microchips, a Digi XBee.RTM. microchip, a Bluetooth Low
Energy microchip, and/or any other suitable communication devices
configured to participate in a mesh network.
[0207] FIG. 2C illustrates an example of a sensor kit network 200C
that is a hierarchical network. In these embodiments, the sensor
kit 100 includes a set of collection devices 206. A collection
device 206 may refer to a non-sensor device that receives sensor
data from a sensor device 104 and routes the sensor data to an edge
device 104, either directly or via another collection device 206.
In embodiments, a hierarchical network may refer to a network
topography where one or more intermediate devices (e.g., collection
devices 206) route data from one or more respective peripheral
devices (e.g., sensor devices 102) to a central device (e.g., edge
device 104). A hierarchical network may include wired and/or
wireless connections. In embodiments, a sensor device 102 may be
configured to communicate with a collection device 206 via any
suitable communication device (e.g., Bluetooth Low Energy
microchips, Wi-Fi microchips, Zigbee microchips, or the like). In
embodiments, hierarchical sensor kit networks may be implemented in
industrial settings 120 where power sources are available to power
the collection devices 206 and/or where the sensors 102 are likely
to be spaced too far apart to support a reliable mesh network.
[0208] The examples of FIGS. 2A-2C are provided for examples of
different topologies of a sensor kit network. These examples are
not intended to limit the types of sensor kit networks 200 that may
be formed by a sensor kit 100. Furthermore, sensor kit networks 200
may be configured as hybrids of star networks, hierarchical
networks, and/or mesh networks, depending on the industrial
settings 120 in which respective sensor kits 200 are being
deployed.
[0209] FIGS. 3A, 3B, 4, and 5--Example Configurations of Sensors,
Edge Devices, and Backend Sy stems
[0210] FIG. 3A illustrates an example IoT sensor 102 (or sensor)
according to embodiments of the present disclosure. Embodiments of
the IoT sensor 102 may include, but are not limited to, one or more
sensing components 302, one or more storage devices 304, one or
more power supplies 306, one or more communication devices 308, and
a processing device 310. In embodiments, the processing device 310
may execute an edge reporting module 312.
[0211] A sensor 102 includes at least one sensing component 302. A
sensing component 302 may be any digital, analog, chemical, and/or
mechanical component that outputs raw sensor data to the processing
device 310. It is appreciated that different types of sensors 102
are fabricated with different types of sensing components. In
embodiments, sensing components 302 of an inertial sensor may
include one or more accelerometers and/or one or more gyroscopes.
In embodiments, sensing components 302 of a temperature sensor may
include one or more thermistors or other temperature sensing
mechanisms. In embodiments, sensing components 302 of a heat flux
sensor may include, for example, thin film sensors, surface mount
sensors, polymer-based sensors, chemical sensors and others. In
embodiments, sensing components 302 of a motion sensor may include
a LIDAR device, a radar device, a sonar device, or the like. In
embodiments, sensing components 302 of an occupancy sensor may
include a surface being monitored for occupancy, a pressure
activated switch embedded under the surface of the occupancy sensor
and/or a piezoelectric element integrated into the surface of the
occupancy sensor, such that an electrical signal is generated when
an object occupies the surface being monitored for occupancy. In
embodiments, sensing components 302 of a humidity sensor may
include a capacitive element (e.g., a metal oxide between to
electrodes) that outputs an electrical capacity value corresponding
to the ambient humidity; a resistive element that includes a salt
medium having electrodes on two sides of the medium, whereby the
variable resistance measured at the electrodes corresponds to the
ambient humidity; and/or a thermal element that includes a first
thermal sensor that outputs a temperature of a dry medium (e.g.,
dry nitrogen) and a second thermal sensor that outputs an ambient
temperature of the sensor's environment, such that the humidity is
determined based on the change, i.e., the delta, between the
temperature in the dry medium and the ambient temperature. In
embodiments, sensing components 302 of a vibration sensor may
include accelerometer components, position sensing components,
torque sensing components, and others. It is appreciated that the
list of sensor types and sensing components thereof is provided for
example. Additional or alternative types of sensors and sensing
components may be integrated into a sensor 102 without departing
from the scope of the disclosure. Furthermore, in some embodiments,
the sensors 102 of a sensor kit 100 may include audio, visual, or
audio/visual sensors, in addition to non-audio/visual sensors 102
(i.e., sensors that do not capture video or audio). In these
embodiments, the sensing components 392 may include a camera and/or
one or more microphones. In some embodiments, the microphones may
be directional microphones, such that a direction of a source of
audio may be determined.
[0212] A storage device 304 may be any suitable medium for storing
data that is to be transmitted to the edge device 104. In
embodiments, a storage device 304 may be a persistent storage
medium, such as a flash memory device. In embodiments, a storage
device 304 may be a transitory storage medium, such as a random
access memory device. In embodiments, a storage device 304 may be a
circuit configured to store charges, whereby the magnitude of the
charge stored by the component is indicative of a sensed value, or
incremental counts. In these embodiments, this type of storage
device 304 may be used where power availability and size are
concerns, and/or where the sensor data is count-based (e.g., a
number of detection events). It is appreciated that any other
suitable storage devices 304 may be used. In embodiments, the
storage device 304 may include a cache 314, such that the cache 314
stores sensor data that is not yet reported to the edge device 104.
In these embodiments, the edge reporting module 312 may clear the
cache 314 after the sensor data being stored in the cache 314 is
transmitted to the edge device 104.
[0213] A power supply 306 is any suitable component that provides
power to the other components of the sensor 102, including the
sensing components 302, storage devices 304, communication devices
306, and/or the processing device 308. In embodiments, a power
supply 306 includes a wired connection to an external power supply
(e.g., alternating current delivered from a power outlet, or direct
current delivered from a battery or solar power supply). In
embodiments, the power supply 306 may include a power inverter that
converts alternating currents to direct currents (or vice-versa).
In embodiments, a power supply 306 may include an integrated power
source, such as a rechargeable lithium ion battery or a solar
element. In embodiments, a power supply 306 may include a
self-powering element, such as a piezoelectric element. In these
embodiments, the piezoelectric element may output a voltage upon a
sufficient mechanical stress or force being applied to the element.
This voltage may be stored in a capacitor and/or may power a
sensing element 302. In embodiments, the power supply may include
an antenna (e.g., a receiver or transceiver) that receives a radio
frequency that energizes the sensor 102. In these embodiments, the
radio frequency may cause the sensor 102 to "wake up" and may
trigger an action by the sensor 102, such as taking sensor
measurements and/or reporting sensor data to the edge device 104. A
power supply 306 may include additional or alternative components
as well.
[0214] In embodiments, a communication device 308 is a device that
enables wired or wireless communication with another device in the
sensor kit network 200. In most sensor kit configurations 100, the
sensors 102 are configured to communicate wirelessly. In these
embodiments, a communication device 308 may include a transmitter
or transceiver that transmits data to other devices in the sensor
kit network 200. Furthermore, in some of these embodiments,
communication devices 308 having transceivers may receive data from
other devices in the sensor kit network 200. In wireless
embodiments, the transceiver may be integrated into a chip that is
configured to perform communication using a respective
communication protocol. In some embodiments, a communication device
308 may be a Zigbee.RTM. microchip, a Digi XBee.RTM. microchip, a
Bluetooth microchip, a Bluetooth Low Energy microchip, a Wi-Fi
microchip, or any other suitable short-range communication
microchip. In embodiments where the sensor kit 200 supports a mesh
network, the communication device 308 may be a microchip that
implements a communication protocol that supports mesh networking
(e.g., ZigBee PRO mesh networking protocol, Bluetooth Mesh,
802.11a/b/g/n/ac, and the like). In these embodiments, a
communication device 308 may be configured to establish the mesh
network and handle the routing of data packets received from other
devices in accordance with the communication protocol implemented
by the communication device 308. In some embodiments, a sensor 102
may be configured with two or more communication devices 308. In
these embodiments, the sensors 102 may be added to different sensor
kit 100 configurations and/or may allow for flexible configuration
of the sensor kit 102 depending on the industrial setting 120.
[0215] In embodiments, the processing device 310 may be a
microprocessor. The microprocessor may include memory (e.g.,
read-only memory (ROM)) that stores computer-executable
instructions and one or more processors that execute the
computer-executable instructions. In embodiments, the processing
device 310 executes an edge reporting module 312. In embodiments,
the edge reporting module 312 is configured to transmit data to the
edge device 104. Depending on the configuration of the sensor kit
network 200 and location of the sensors 102 with respect to the
edge device 104, the edge reporting module 312 may transmit data
(e.g., sensor data) either directly to the edge device 104, or to
an intermediate device (e.g., a collection device 206 or another
sensor device 102) that routes the data towards the edge device
104. In embodiments, the edge reporting module 312 obtains raw
sensor data from a sensing component 302 or from a storage device
304 and packetizes the raw sensor data into a reporting packet
320.
[0216] FIG. 3B illustrates an example reporting packet 320
according to some embodiments of the present disclosure. In some of
these embodiments, the edge reporting module 312 may populate a
reporting packet template to obtain a reporting packet 320. In
embodiments, a reporting packet 320 may include a first field 322
indicating a sensor ID of the sensor 102 and a second field 326
indicating the sensor data. Additionally, the reporting packet 320
may include additional fields, such as a routing data field 324
indicating a destination of the packet (e.g., an address or
identifier of the edge device 104), a time stamp field 328
indicating a time stamp, and/or a checksum field 330 indicating a
checksum (e.g., a hash value of the contents of the reporting
packet). The reporting packet may include additional or alternative
fields (e.g., error codes) without departing from the scope of the
disclosure.
[0217] Referring back to FIG. 3A, in embodiments, the edge
reporting module 312 may generate a reporting packet 320 for each
instance of sensor data. Alternatively, the edge reporting module
312 may generate a reporting packet 320 that includes a batch of
sensor data (e.g., the previous N sensor readings or all the sensor
readings maintained in a cache 314 of the sensor 102 since the
cache 314 was last purged). Upon generating a reporting packet 320,
the edge reporting module 312 may output the reporting packet 320
to the communication device 308, which transmits the reporting
packet 320 to the edge device 104 (either directly or via one or
more intermediate devices). The edge reporting module 312 may
generate and transmit reporting packets 320 at predetermined
intervals (e.g., every second, every minute, every hour),
continuously, or upon being triggered (e.g., upon being activated
via the power supply or upon being command by the edge device
104).
[0218] In embodiments, the edge reporting module 312 instructs the
sensing component(s) 302 to capture sensor data. In embodiments,
the edge reporting module 312 may instruct a sensing component 302
to capture sensor data at predetermined intervals. For example, the
edge reporting module 312 may instruct the sensing component 302 to
capture sensor data every second, every minute, or every hour. In
embodiments, the edge reporting module 312 may instruct a sensing
component 302 to capture sensor data upon the power supply 306
being energized. For example, the power supply 306 may be energized
by a radio frequency or upon a pressure-switch being activated and
closing a circuit. In embodiments, the edge reporting module 312
may instruct a sensing component 302 to capture sensor data in
response to receiving a command to report sensor data from the edge
device 104 or a human user (e.g., in response to the user pressing
a button).
[0219] In embodiments, a sensor 102 includes a housing (not shown).
The sensor housing may have any suitable form factor. In
embodiments where the sensor 102 is being used outdoors, the sensor
may have a housing that is waterproof and/or resistant to extreme
cold and/or extreme heat. In embodiments, the housing may have
suitable coupling mechanisms to removably couple to an industrial
component.
[0220] The foregoing is an example of a sensor 102. The sensor 102
may have additional or alternative components without departing
from the scope of the disclosure.
[0221] FIG. 4 illustrates an example of an edge device 104. In
embodiments, the edge device 104 may include a storage system 402,
a communication system 404, and a processing system 406. The edge
device 104 may include additional components not shown, such as a
power supply, a user interface, and the like.
[0222] The storage system 402 includes one or more storage devices.
The storage devices may include persistent storage mediums (e.g.,
flash memory drive, hard disk drive) and/or transient storage
devices (e.g., RAM). The storage system 402 may store one or more
data stores. A data store may include one or more databases,
tables, indexes, records, filesystems, folders and/or files. In the
illustrated embodiments, the storage device stores a configuration
data store 410, a sensor data store 412, and a model data store
414. A storage system 402 may store additional or alternative data
stores without departing from the scope of the disclosure.
[0223] In embodiments, the configuration data store 410 stores data
relating to the configuration of the sensor kit 100, including the
devices of the sensor kit 100. In some embodiments, the
configuration data store 410 may maintain a set of device records.
The device records may indicate a device identifier that uniquely
identifies a device of the sensor kit 100. The device records may
further indicate the type of device (e.g., a sensor, a collection
device, a gateway device, etc.). In embodiments where the network
paths from each device to the edge device 104 do not change, a
device record may also indicate the network path of the device to
the edge device 104 (e.g., any intermediate devices in the device's
network path). In the case that a device record corresponds to a
sensor 102, the device record may indicate the type of sensor
(e.g., a sensor type identifier) and/or a type of data that is
provided by the sensor 102.
[0224] In embodiments, the configuration data store 410 may
maintain a set of sensor type records, where each record
corresponds to a different type of sensor 102 in the sensor kit
100. A sensor type record may indicate a type identifier that
identifies the type of sensor and/or the type of sensor data
provided by the sensor. In embodiments, a sensor type record may
further indicate relevant information relating to the sensor data,
including maximum or minimum values of the sensor data, error codes
output by sensors 102 of the sensor type, and the like.
[0225] In embodiments, the configuration data store 410 may
maintain a map of the sensor kit network 200. The map of the sensor
kit network 200 may indicate a network topology of the sensor kit
network 200, including network paths of the collection of devices
in the sensor kit 100. In some embodiments, the map may include
physical locations of the sensors as well. The physical location of
a sensor 102 may be defined as a room or area that the sensor 102
is in, a specific industrial component that the sensor 102 is
monitoring, a set of coordinates relative of the edge device 104
(e.g., x, y, z coordinates relative to the edge device 104, or an
angle and distance of the sensor 102 relative to the edge device
104), an estimated longitude and latitude of the sensor 102, or any
other suitable format of relative or absolute location
determination and/or measurement.
[0226] In embodiments, a sensor data store stores 412 stores sensor
data collected from the sensors 102 of the sensor kit 100. In
embodiments, the sensor data store 412 maintains sensor data that
is collected over a period of time. In some of these embodiments,
the sensor data store 412 may be a cache that stores sensor data
until it is reported and backed up at the backend system 150. In
these embodiments, the cache may be cleared when sensor data is
reported to the backend system 150. In some embodiments, the sensor
data store 412 stores all sensor data collected by the sensor kit
412. In these embodiments, the sensor data store 412 may provide a
backup for all the sensor data collected by the sensor kit 100 over
time, thereby ensuring that the owner of the sensor kit 100
maintains ownership of its data.
[0227] In embodiments, a model data store 414 stores
machine-learned models. The machine-learned models may include any
suitable type of models, including neural networks, deep neural
networks, recursive neural networks, Bayesian neural networks,
regression-based models, decision trees, prediction trees,
classification trees, Hidden Markov Models, and/or any other
suitable types of models. A machine-learned model may be trained on
training data, which may be expert generated data, historical data,
and/or outcome-based data. Outcome-based data may be data that is
collected after a prediction or classification is made that
indicates whether the prediction or classification was correct or
incorrect and/or a realized outcome. A training data instance may
refer to a unit of training data that includes a set of features
and a label. In embodiments, the label in a training data instance
may indicate a condition of an industrial component or an
industrial setting 120 at a given time. Examples of conditions will
vary greatly depending on the industrial setting 120 and the
conditions that the machine-learned model is being trained to
predict or classify. Examples of labels in a manufacturing facility
may include, but are not limited to, no issues detected, a
mechanical failure of a component, an electrical failure of a
component, a chemical leak detected, and the like. Examples of
labels in a mining facility may include, but are not limited to, no
issues detected, an oxygen deficiency, the presence of a toxic gas,
a failing structural component, and the like. Examples of labels in
an oil and/or gas facility (e.g., oil field, gas field, oil
refinery, pipeline) may include, but are not limited to, no issues
detected, a mechanical failure of a component (e.g., a failed valve
or failed O-ring), a leak, and the like. Examples of labels in an
indoor agricultural facility may include, but are not limited to,
no issues detected, a plant died, a plant wilted, a plant turned a
certain color (e.g., brown, purple, orange, or yellow), mold found,
and the like. In each of these examples, there are certain features
that may be relevant to a condition and some features that may have
little or no bearing on the condition. Through a machine-learning
process (which may be performed at the backend system 150 or
another system), the model is trained to determine predictions or
classifications based on a set of features. Thus, the set of
features in a training data instance may include sensor data that
is temporally proximate to a time when a condition of the
industrial component or industrial setting 120 occurred (e.g., the
label associated with the industrial component or industrial
setting 120).
[0228] In embodiments, the machine-learned models may include
prediction models that are used to predict potential issues
relating to an industrial component being monitored. In some of
these embodiments, a machine-learned model may be trained on
training data (expert generated data and/or historical data) that
corresponds to one or more conditions relating to a particular
component. In some of these embodiments, the training data sets may
include sensor data corresponding to scenarios where maintenance or
some intervening action was later required and sensor data
corresponding to scenarios where maintenance or some intervening
action was ultimately not required. In these example embodiments,
the machine-learned model may be used to determine a prediction of
one or more potential issues that may arise with respect to one or
more industrial components being monitored and/or the industrial
setting 120 being monitored.
[0229] In embodiments, the machine-learned models may include
classification models that classify a condition of an industrial
component being monitored and/or the industrial setting 120. In
some of these embodiments, a machine-learned model may be trained
on training data (e.g., expert generated data and/or historical
data) that corresponds to one or more conditions relating to a
particular component. In some of these embodiments, the training
data sets may include sensor data corresponding to scenarios where
respective industrial components and/or respective industrial
settings 120 were operating in a normal condition and sensor data
where the respective industrial components and/or respective
industrial settings 120 were operating in an abnormal condition. In
training data instances where there was an abnormal condition, the
training data instance may include a label indicating the type of
abnormal condition. For example, a training data instance
corresponding to an indoor agricultural facility that was deemed
too humid for ideal growing conditions may include a label that
indicates the facility was too humid.
[0230] In embodiments, the communication system 404 includes two or
more communication devices, including at least one internal
communication device that communicates with the sensor kit network
200 and at least one external communication device that
communicates with a public communication network (e.g., the
Internet) either directly or via a gateway device. The at least one
internal communication devices may include Bluetooth chips, Zigbee
chips, XBee chips, Wi-Fi chips, and the like. The selection of the
internal communication devices may depend on the environment of the
industrial setting 120 and the impacts thereof on the sensors 102
to be installed therein (e.g., whether the sensors 102 have
reliable power sources, whether the sensors 102 will be spaced in
proximity to one another, whether the sensors 102 need to transmit
through walls, and the like). The external communication devices
may perform wired or wireless communication. In embodiments, the
external communication devices may include cellular chipsets (e.g.,
4G or 5G chipsets), Ethernet cards, satellite communication cards,
or other suitable communication devices. The external communication
device(s) of an edge device 104 may be selected based on the
environment of the industrial setting 120 (e.g., indoors v.
outdoors, thick walls that prevent wireless communication v. thin
walls that allow wireless communication, located near cellphone
towers v. located in remote areas) and the preferences of an
operator of the industrial setting 120 (e.g., the operator allows
the edge device 104 to access a private network of the industrial
setting 120, or the operator does not allow the edge device 104 to
access a private network of the industrial setting 120).
[0231] In embodiments, the processing system 406 may include one or
more memory devices (e.g., ROM and/or RAM) that store
computer-executable instructions and one or more processors that
execute the computer-executable instructions. The processing system
406 may execute one or more of a data processing module 420, an
encoding module 422, a quick-decision AI module 424, a notification
module 426, a configuration module 428, and a distributed ledger
module 430. The processing system 406 may execute additional or
alternative modules without departing from the scope of the
disclosure. Furthermore, the modules discussed herein may include
submodules that perform one or more functions of a respective
module.
[0232] In embodiments, the data processing module 420 receives
sensor data from the sensor kit network 200 and performs one or
more data processing operations on the received sensor data. In
embodiments, the data processing module 420 receives reporting
packets 320 containing sensor data. In some of these embodiments,
the data processing module 420 may filter data records that are
duplicative (e.g., filtering out one out of two reporting packets
320 received from two respective sensors monitoring the same
component for redundancy). The data processing module 420 may
additionally or alternatively filter and/or flag reporting packets
320 containing sensor data that is clearly erroneous (e.g., sensor
not within a tolerance range given the type of sensor 102 or
contains an error code). In embodiments, the data processing module
420 may store and/or index the sensor data in the sensor data
store.
[0233] In embodiments, the data processing module 420 may aggregate
sensor data received over a period of time from the sensors 102 of
the sensor kit 100 or a subset thereof and may transmit the sensor
data to the backend system 150. In transmitting sensor data to the
backend system 150, the data processing module 420 may generate a
sensor kit reporting packet that includes one or more instances of
sensor data. The sensor data in the sensor kit reporting packet may
be compressed or uncompressed. In embodiments, the sensor kit
reporting packet may indicate a sensor kit identifier that
identifies the source of the data packet to the backend system 150.
In embodiments, the data processing module 420 may transmit the
sensor data upon receipt of the sensor data from a sensor 102, at
predetermined intervals (e.g., every second, every minute, every
hour, every day), or in response to a triggering condition (e.g., a
prediction or classification that there is an issue with an
industrial component or the industrial setting 120 based on
received sensor data). In some embodiments, the sensor data may be
encoded/compressed, such that sensor data collected from multiple
sensors 102 and/or over a period of time may be more efficiently
transmitted. In embodiments, the data processing module 420 may
leverage the quick-decision AI module 424 to determine whether the
industrial components of the industrial setting 120 and/or the
industrial setting 120 itself is likely in a normal condition. If
the quick-decision AI module 424 determines that the industrial
components and/or the industrial setting 120 are in a normal
condition with a high degree of certainty, then the data processing
module 420 may delay or forgo transmitting the sensor data used to
make the classification to the backend system 150. Additionally or
alternatively, if the quick-decision AI module 424 determines that
the industrial components and/or the industrial setting 120 are in
a normal condition with a high degree of certainty, then the data
processing module 420 may compress the sensor data and may be
compressed at a greater rate. The data processing module 420 may
perform additional or alternative functions without departing from
the scope of the disclosure.
[0234] In embodiments, the encoding module 422 receives sensor data
and may encode, compress, and/or encrypt the sensor data. The
encoding module 422 may employ other techniques to compress the
sensor data. In embodiments, the encoding module 422 may employ
horizontal or compression techniques to compress the sensor data.
For example, the encoding module 422 may use the Lempel-Zev-Welch
algorithm or variations thereof. In some embodiments, the encoding
module 522 may represent sensor data in an original integer or
"counts format" and with relevant calibration coefficients and
offsets at the time of collection. In these embodiments, the
coefficients and offsets may be coalesced at the time of collection
when a precise signal path is known, such that one floating-point
coefficient and one integer offset is stored for each channel.
[0235] In embodiments, the encoding module 422 may employ one or
more codecs to compress the sensor data. The codecs may be
proprietary codecs and/or publicly available codecs. In some
embodiments, the encoding module 422 may use a media compression
codec (e.g., a video compression codec) to compress the sensor
data. For example, the encoding module 422 may normalize the sensor
data into values that fall within a range and format of a media
frame (e.g., normalizing sensor data into acceptable pixel values
for inclusion into a video frame) and may embed the normalized
sensor data into the media frame. The encoding module 422 may embed
the normalized sensor data collected from the sensors 102 of the
sensor kit 100 into the media frame according to a predefined
mapping (e.g., a mapping of respective sensors 102 to one or more
respective pixels in a media frame). The encoding module 422 may
generate a set of consecutive media frames in this manner and may
compress the media frames using a media codec (an H.264/MPEG-4
codec, an H.265/MPEG-H codec, an H.263/MPEG-4 codec, proprietary
codecs, and the like) to obtain a sensor data encoding. The
encoding module 422 may then transmit sensor data encoding to the
backend system, which may decompress and recalculate the sensor
data based on the normalized values. In these embodiments, the
codec used for compression and the mappings of sensors to pixels
may be selected to reduce lossiness or to increase compression
rates. Furthermore, the foregoing technique may be applied to
sensor data that tends to be more static and less changing between
samplings and/or where sensor data collected from different sensors
tend to have little variation when sampled at the same time. The
encoding module 422 may employ additional or alternative
encoding/compression techniques without departing from the scope of
the disclosure.
[0236] In embodiments, the quick-decision AI module 424 may utilize
a limited set of machine-learned models to generate predictions
and/or classifications of a condition of an industrial component
being monitored and/or of the industrial setting 120 being
monitored. In embodiments, the quick-decision AI module 424 may
receive a set of features (e.g., one or more sensor data values)
and request for a specific type of prediction or classification
based thereon. In embodiments, the quick-decision AI module 424 may
leverage a machine-learned model corresponding to the requested
prediction or classification. The quick-decision AI module 424 may
generate a feature vector based on the received features, such that
the feature vector includes one or more sensor data values obtained
from one or more sensors 102 of the sensor kit 100. The
quick-decision AI module 424 may feed the feature vector to the
machine-learned model. The machine-learned model may output a
prediction or classification and a degree of confidence in the
prediction or classification. In embodiments, the quick-decision AI
module 424 may output the prediction or classification to the data
processing module 420 (or another module that requested a
prediction or classification). For example, in embodiments the data
processing module 420 may use classifications that the industrial
components and/or the industrial setting 120 are in a normal
condition to delay or forgo transmission of sensor data and/or to
compress sensor data. In embodiments, the data processing module
420 may use a prediction or classification that the industrial
components and/or the industrial setting 120 are likely to
encounter a malfunction to transmit uncompressed sensor data to the
backend system 150, which may further analyze the sensor data
and/or notify a human user of a potential issue.
[0237] In embodiments, the notification module 426 may provide
notifications or alarms to users based on the sensor data. In some
of these embodiments, the notification module 426 may apply a set
of rules that trigger a notification or alarm if certain conditions
are met. The conditions may define sensor data values that are
strongly correlated with an undesirable (e.g., emergency)
condition. Upon receiving sensor data from the data processing
module 420, the notification module 426 may apply one or more rules
to the sensor data. If the conditions to trigger an alarm or
notification are met, the notification module 426 may issue an
alarm or notification to a human user. The manner by which an alarm
or notification is provided to the human user (e.g., to a user
device, or triggering an audible alarm) may be predefined or, in
some embodiments, may be defined by an operator of the industrial
setting 120.
[0238] In embodiments, the configuration module 428 configures the
sensor kit network 200. In embodiments, the configuration module
428 may transmit configuration requests to the other devices in the
sensor kit 100, upon the sensors 102, edge device 104, and any
other devices being installed in the industrial setting 120. In
some of these embodiments, the sensors 102 and/or other devices may
establish a mesh network or a hierarchical network in response to
the configuration requests. In embodiments, the sensors 102 and
other devices in the sensor kit network may respond to the
configuration requests, in response to the configuration requests.
In embodiments, the configuration module 428 may generate device
records corresponding to the devices that responded based on the
device IDs of those devices and any additional data provided in the
responses to the configuration requests.
[0239] In embodiments, the configuration module 428 adds new
devices to the sensor kit 100. In these embodiments, the
configuration module 428 adds new sensors 102 to the sensor kit 100
post-installation in a plug-and-play-like manner. In some of these
embodiments, the communication devices 404, 308 of the edge device
104 and the sensors 102 (or other devices to be added to the sensor
kit 100) may include respective short-range communication
capabilities (e.g., near-field communication (NFC) chips). In these
embodiments, the sensors 102 may include persistent storage that
stores identifying data (e.g., a sensor id value) and any other
data that would be used to add the sensor to the sensor kit (e.g.,
device type, supported communication protocols, and the like). In
response to a user initiating a post-installation addition to the
sensor kit 100 (e.g., the user pressing a button on the edge device
104 and/or bringing the sensor 102 into the vicinity of the edge
device 104), the configuration module 428 may cause the
communication system 404 to emit a signal (e.g., a radio
frequency). The emitted signal may trigger a sensor 102 proximate
enough to receive the signal to transmit its sensor ID and any
other suitable configuration data (e.g., device type, communication
protocols, and the like). In response to the sensor 102
transmitting its configuration data (sensor ID and other relevant
configuration data) to the edge device 104, the configuration
module 428 may add the new sensor 102 to the sensor kit 102. In
embodiments, adding the sensor 102 to the sensor kit 104 may
include generating a new device record corresponding to the new
sensor 102 based on the sensor id updating the configuration data
store 410 with the new device record. The configuration module 428
may add a new sensor 102 to the sensor kit 100 in any other
suitable manner.
[0240] In embodiments, the edge device 104 may include a
distributed ledger module 430. In embodiments, the distributed
ledger module 430 may be configured to update a distributed ledger
162 with sensor data captured by the sensor kit 100. In
embodiments, the distributed ledger may be distributed amongst a
plurality of node computing devices 160. As discussed, in
embodiments, a distributed ledger 162 is comprised of a set of
linked data structures (e.g., blocks, data records, etc.). For
purposes of explanation, the data structures will be referred to as
blocks.
[0241] As discussed, each block may include a header that includes
a unique ID of the block and a body that includes the data that is
stored in the block and a pointer of a parent block. In
embodiments, the pointer in the block is the block ID of a parent
block of the block. The data stored in a respective block can be
sensor data captured by a respective sensor kit 100. Depending on
the implementation, the types of sensor data and the amount of
sensor data stored in a respective body of a block may vary. For
example, a block may store a set of sensor measurements from one or
more types of sensors 102 in the sensor kit 100 captured over a
period of time (e.g., sensor data 102 captured from all of the
sensors 102 in the sensor kit 100 over a period one hour or one
day) and metadata relating thereto (e.g., sensor IDs of each sensor
measurement and a timestamp of each sensor measurement or group of
sensor measurements). In some embodiments, a block may store sensor
measurements determined to be anomalous (e.g., outside a standard
deviation of expected sensor measurements or deltas in sensor
measurements that are above a threshold) and/or sensor measurements
indicative of an issue or potential issue, and related metadata
(e.g., sensor IDs of each sensor measurement and a timestamp of
each sensor measurement or group of sensor measurements). In some
embodiments, the sensor data stored in a block may be compressed
and/or encoded sensor data, such that the encoding module 422
compresses/encodes the sensor data into a more compact format. In
embodiments, the distributed ledger module 430 may generate a hash
of the body, such that the contents of the body (e.g., block ID of
the parent block and the sensor data) are hashed and cannot be
altered without changing the value of the hash. In embodiments, the
distributed ledger module 430 may encrypt the content within the
block, so that the content may not be read by unauthorized
devices.
[0242] In embodiments, the distributed ledger module 430 generates
a block in response to a triggering event. Examples of triggering
events may include a predetermined time (e.g., every minute, every
hour, every day), when a potential issue is classified or
predicted, when one or more sensor measurements are outside of a
tolerance threshold, or the like. In response to the triggering
event, the distributed ledger module 430 may generate a block based
on sensor data that is to be reported. Depending on the
configuration of the server kit 100 and the intended use of the
distributed ledger 162, the amount of data and type of data that is
included in a block may vary. For example, in a manufacturing or
resource extraction setting such as the manufacturing facility 1700
or the underwater industrial setting 1800, the distributed ledger
162 may be used to demonstrate functional machinery and/or to
predict maintenance needs. In this example, the distributed ledger
module 430 may be accessible by insurance providers to set
insurance rates and/or issue refunds. Thus, in this example, the
distributed ledger module 430 may include any sensor measurements
(and related metadata) that are outside of a tolerance threshold or
instance where an issue is classified or predicted. In another
example, the distributed ledger may be accessible by a regulatory
body to ensure that a facility is operating in accordance with one
or more regulations. In these embodiments, the distributed ledger
module 430 may store a set of one or more sensor measurements (and
related metadata) in a block, such that the sensor measurements may
be analyzed by the regulatory agency. In some of these embodiments,
the sensor measurements may be compressed to store more sensor data
in a single block. In response to generating a block, the
distributed ledger module 430 may transmit the block to one or more
node computing devices 160. Upon the block being verified (e.g.,
using a consensus mechanism), each node computing device 160 may
update the distributed ledger 162 with the new block.
[0243] As discussed, in some embodiments the distributed ledger may
further include smart contracts. Once written, a smart contract may
be encoded in a block and deployed to the distributed ledger 162.
The address of the smart contract (e.g., the block ID of the block
containing the smart contract) may be provided to one or more
parties to the smart contract, such that respective parties may
invoke the smart contract using the address. In some of these
embodiments, the address of the smart contract may be provided to
the distributed ledger module 430, such that the distributed ledger
module 430 may report items to the smart contract. In some
embodiments, the distributed ledger module 430 may leverage the API
of a smart contract to report the items to the smart contract.
[0244] In example implementations discussed above, an insurer may
utilize a smart contract to allow insured facility owners and/or
operators to demonstrate that the equipment in the facility is
functioning properly. In some embodiments, the smart contract may
trigger the issuance of rebates or refunds on portions of insurance
premiums when an owner and/or operator of a facility provides
sufficient sensor data that indicates the facility is operating
without issue. In some of these embodiments, the smart contract may
include a first condition that requires a certain amount of sensor
data to be reported by a facility and a second condition that each
instance of the sensor data equals a value (e.g., no classified or
predicted issues) or range of values (e.g., all sensor measurements
within a predefined range of values). In some embodiments, the
action may be to deposit funds (e.g., a wire transfer or
cryptocurrency) into an account in response to the first and second
conditions being met. In this example, the distributed ledger
module 430 may write blocks containing sensor data to the
distributed ledger 162. The distributed ledger module 430 may also
provide the addresses of these blocks to the smart contract (e.g.,
via an API of the smart contract). Upon the smart contract
verifying the first and second conditions of the contract, the
smart contract may initiate the transfer of funds from an account
of the insurer to the account of the insured.
[0245] In another example discussed above, a regulatory body (e.g.,
a state, local, or federal regulatory agency) may utilize a smart
contract that monitors facilities (e.g., food inspection
facilities, pharmaceutical manufacturing facilities, indoor
agricultural facilities, offshore oil extraction facilities, or the
like) based on reported sensor data to ensure compliance with one
or more regulations. In embodiments, the smart contract may be
configured to receive and verify the sensor data from a facility
(e.g., via an API of the smart contract), and in response to
verifying the sensor data issues a compliance token (or
certificate) to an account of the facility owner. In some of these
embodiments, the smart contract may include a first condition that
requires a certain amount of sensor data to be reported by a
facility and a second condition that requires the sensor data to be
compliant with the reporting regulations. In this example, the
distributed ledger module 430 may write blocks containing sensor
data to the distributed ledger. The sensor kit 100 may also provide
the addresses of these blocks to the smart contract (e.g., using an
API of the smart contract). Upon the smart contract verifying the
first and second conditions of the contract, the smart contract may
generate a token indicating compliance by the facility operator,
and may initiate the transfer of funds to an account (e.g., a
digital wallet) associated with the facility.
[0246] FIG. 5 illustrates an example backend system 150 according
to some embodiments of the present disclosure. In embodiments, the
backend system 150 may be implemented as a cloud service that is
executed at one or more physical server devices. In embodiments,
the backend system 150 may include a storage system 502, a
communication system 504, and a processing system 506. The backend
system 150 may include additional components not shown.
[0247] A storage system 502 includes one or more storage devices.
The storage devices may include persistent storage mediums (e.g.,
flash memory drive, hard disk drive) and/or transient storage
devices (e.g., RAM). The storage system 502 may store one or more
data stores. A data store may include one or more databases,
tables, indexes, records, filesystems, folders and/or files. In the
illustrated embodiments, the storage system 502 stores a sensor kit
data store 510 and a model data store 512. A storage system 502 may
store additional or alternative data stores without departing from
the scope of the disclosure.
[0248] In embodiments, the sensor kit data store 510 stores data
relating to respective sensor kits 100. In embodiments, the sensor
kit data store 510 may store sensor kit data corresponding to each
installed sensor kit 100. In embodiments, the sensor kit data may
indicate the devices in a sensor kit 100, including each sensor 102
(e.g., a sensor ID) in the sensor kit 100. In some embodiments, the
sensor kit data may indicate the sensor data captured by the sensor
kit 100. In some of these embodiments, the sensor kit data may
identify each instance of sensor data captured by the sensor kit
100, and for each instance of sensor data, the sensor kit data may
indicate the sensor 102 that captured the sensor data and, in some
embodiments, a time stamp corresponding to the sensor data.
[0249] In embodiments, the model data store 512 stores
machine-learned models that are trained by the AI system 524 based
on training data. The machine-learned models may include prediction
models and classification models. In embodiments, the training data
used to train a particular model includes data collected from one
or more sensor kits 100 that monitor the same type of industrial
setting 120. The training data may additionally or alternatively
may include historical data and/or expert generated data. In
embodiments, each machine-learned model may pertain to a respective
type of industrial setting 120. In some of these embodiments, the
AI system 524 may periodically update a machine-learned model
pertaining to a type of industrial setting 120 based on sensor data
collected from sensor kits 100 monitoring those types of industrial
setting 120 and outcomes obtained from those industrial setting
120. In embodiments, machine-learned models pertaining to a type of
industrial setting 120 may be provided to the edge devices 104 of
sensor kits 100 monitoring that type of industrial setting 120.
[0250] In embodiments, a communication system 504 includes one or
more communication devices, including at least one external
communication device that communicates with a public communication
network (e.g., the Internet) ether. The external communication
devices may perform wired or wireless communication. In
embodiments, the external communication devices may include
cellular chipsets (e.g., 4G or 5G chipsets), Ethernet cards and/or
Wi-Fi cards, or other suitable communication devices.
[0251] In embodiments, the processing system 506 may include one or
more memory devices (e.g., ROM and/or RAM) that store
computer-executable instructions and one or more processors that
execute the computer-executable instructions. The processors may
execute in a parallel or distributed manner. The processors may be
located in the same physical server device or in different server
devices. The processing system 506 may execute one or more of a
decoding module 520, a data processing module 522, an AI module
524, a notification module 526, an analytics module 528, a control
module 530, a dashboard module 532, a configuration module 534, and
a distributed ledger management module 536. The processing system
406 may execute additional or alternative modules without departing
from the scope of the disclosure. Furthermore, the modules
discussed herein may include submodules that perform one or more
functions of a respective module.
[0252] In embodiments, a sensor kit 100 may transmit encoded sensor
kit packets containing sensor data to the backend system 150. In
these embodiments, the decoding module 520 may receive encoded
sensor data from an edge device 104 and may decrypt, decode, and/or
decompress the encoded sensor kit packets to obtain the sensor data
and metadata relating to the received sensor data (e.g., a sensor
kit id and one or more sensor ids of sensors that captured the
sensor data). The decoding module 520 may output the sensor data
and any other metadata to the data processing module 522.
[0253] In embodiments, the data processing module 522 may process
the sensor data received from the sensor kits 100. In some
embodiments, the data processing module 522 may receive the sensor
data and may store the sensor data in the sensor kit data store 510
in relation to the sensor kit 100 that provided to the sensor data.
In embodiments, the data processing system 522 may provide
AI-related requests to the AI module 524. In these embodiments, the
data processing system 522 may extract relevant sensor data
instances from the received sensor data and may provide the
extracted sensor data instances to the AI module 524 in a request
that indicates the type of request (e.g., what type of prediction
or classification) and the sensor data to be used. In the event a
potential issue is predicted or classified, the data processing
module 522 may execute a workflow associated with the potential
issue. A workflow may define the manner by which a potential issue
is handled. For instance, the workflow may indicate that a
notification should be transmitted to a human user, a remedial
action should be initiated, and/or other suitable actions. The data
processing module 522 may perform additional or alternative
processing tasks without departing from the scope of the
disclosure.
[0254] In embodiments, the AI module 524 trains machine-learned
models that are used to make predictions or classifications. The
machine-learned models may include any suitable type of models,
including neural networks, deep neural networks, recursive neural
networks, Bayesian neural networks, regression-based models,
decision trees, prediction trees, classification trees, Hidden
Markov Models, and/or any other suitable types of models. The AI
module 524 may train a machine-learned model on a training data
set. A training data set may include expert-generated data,
historical data, and/or outcome-based data. Outcome-based data may
be data that is collected after a prediction or classification is
made that indicates whether the prediction or classification was
correct or incorrect and/or a realized outcome. A training data
instance may refer to a unit of training data that includes a set
of features and a label. In embodiments, the label in a training
data instance may indicate a condition of an industrial component
or an industrial setting 120 at a given time. Examples of
conditions will vary greatly depending on the industrial setting
120 and the conditions that the machine-learning model is being
trained to predict or classify. Examples of labels in a
manufacturing facility may include, but are not limited to, no
issues detected, a mechanical failure of a component, an electrical
failure of a component, a chemical leak detected, and the like.
Examples of labels in a mining facility may include, but are not
limited to, no issues detected, an oxygen deficiency, the presence
of a toxic gas, a failing structural component, and the like.
Examples of labels in an oil and/or gas facility (e.g., oil field,
gas field, oil refinery, pipeline) may include, but are not limited
to, no issues detected, a mechanical failure of a component (e.g.,
a failed valve or failed 0-ring), a leak, and the like. Examples of
labels in an indoor agricultural facility may include, but are not
limited to, no issues detected, a plant died, a plant wilted, a
plant turned a certain color (e.g., brown, purple, orange, or
yellow), mold found, and the like. In each of these examples, there
are certain features that may be relevant to a condition and some
features that may have little or no bearing on the condition. In
embodiments, the AI module 524 may reinforce the machine-learned
models as more sensor data and outcomes relating to the
machine-learned models are received. In embodiments, the
machine-learned models may be stored in the model data store 512.
Each model may be stored with a model identifier, which may be
indicative of (e.g., mapped to) the type of industrial setting 120
that the model makes, the type of prediction or classification made
by the model, and the features that the model receives. In some
embodiments, one or more machine-learned models (and subsequent
updates thereto) may be pushed to respective sensor kits 100,
whereby the edge devices 104 of the respective sensor kits 100 may
use one or more machine-learned model to make predictions and/or
classifications without having to rely on the backend system
150.
[0255] In embodiments, the AI module 524 receives requests for
predictions and/or classifications and determines predictions
and/or classifications based on the requests. In embodiments, a
request may indicate a type of prediction or classification that is
being requested and may include a set of features for making the
prediction or classification. In response to the request, the AI
module 524 may select a machine-learned model to leverage based on
the type of prediction or classification being requested, whereby
the selected model receives a certain set of features. The AI
module 524 may then generate a feature vector that includes one or
more instances of sensor data and may feed the feature vector into
the selected model. In response to the feature vector, the selected
model may output a prediction or classification, and a degree of
confidence (e.g., a confidence score) in the prediction or
classification. The AI module 524 may output the prediction or
classification, as well as the degree of confidence therein, to the
module that provided the request.
[0256] In embodiments, the notification module 526 may issue
notifications to users and/or respective industrial setting 120
when an issue is detected in a respective setting. In embodiments,
a notification may be sent to a user device of a user indicating
the nature of the issue. The notification module 526 may implement
an API (e.g., a REST API), whereby a user device of a user
associated with the industrial setting 120 may request
notifications from the backend system 150. In response to the
request, the notification module 526 may provide any notifications,
if any, to the user device. In embodiments, a notification may be
sent to a device located at an industrial setting 120, whereby the
device may raise an alarm at the industrial setting 120 in response
to the industrial setting 120.
[0257] In embodiments, the analytics module 528 may perform
analytics related tasks on sensor data collected by the backend
system 150 and stored in the sensor kit data store 510. In
embodiments, the analytics tasks may be performed on sensor data
received from individual sensor kits. Additionally, or
alternatively, the analytics tasks may be performed on sensor data
Examples of analytics tasks that may be performed on sensor data
obtained from various sensor kits 100 monitoring different
industrial setting 120. Examples of analytics tasks may include
energy utilization analytics, quality analytics, process
optimization analytics, financial analytics, predictive analytics,
yield optimization analytics, fault prediction analytics, scenario
planning analytics, and many others.
[0258] In embodiments, the control module 530 may control one or
more aspects of an industrial setting 120 based on a determination
made by the AI system 524. In embodiments, the control module 530
may be configured to provide commands to a device or system at the
industrial setting 120 to take a remedial action in response to a
particular issue being detected. For example, the control module
530 may issue a command to a manufacturing facility to stop an
assembly line in response to a determination that a critical
component on the assembly line is likely failing or likely failed.
In another example, the control module 530 may issue a command to
an agricultural facility to activate a dehumidifier in response to
a determination that the humidity levels are too high in the
facility. In another example, the control module 530 may issue a
command to shut a valve in an oil pipeline in response to a
determination that a component in the oil pipeline downstream to
the valve is likely failing or likely failed. For a particular
industrial setting 120, the control module 530 may perform remedial
actions defined by a human user associated with the industrial
setting 120, such that the human user may define what conditions
may trigger the remedial action.
[0259] In embodiments, the dashboard module 532 presents a
dashboard to human users via a user device 140 associated with the
human user. In embodiments, the dashboard provides a graphical user
interface that allows the human user to view relating to a sensor
kit 100 with which the human user is associated (e.g., an employee
at the industrial setting 120). In these embodiments, the dashboard
module 532 may retrieve and display raw sensor data provided by the
sensor kit, analytical data relating to the sensor data provided by
the sensor kit 100, predictions or classifications made by the
backend system 150 based on the sensor data, and the like.
[0260] In embodiments, the dashboard module 532 allows human users
to configure aspects of the sensor kits 100. In embodiments, the
dashboard module 532 may present a graphical user interface that
allows a human user to configure one or more aspects of a sensor
kit 100 with which the human user is associated. In embodiments,
the dashboard may allow a user to configure alarm limits with
respect to one or more sensor types and/or conditions. For example,
a user may define a temperature value at which a notification is
sent to a human user. In another example, the user may define a set
of conditions, which if predicted by the AI module and/or the edge
device, trigger an alarm. In embodiments, the dashboard may allow a
user to define which users receive a notification when an alarm is
triggered. In embodiments, the dashboard may allow a user to
subscribe to additional features of the backend system 150 and/or
an edge device 104.
[0261] In embodiments, the dashboard may allow a user to add one or
more subscriptions to a sensor kit 100. The subscriptions may
include access to backend services and/or edge services. A user may
select a service to add to a sensor kit 100 and may provide payment
information to pay for the services. Upon verification of the
payment information, the backend system 150 may provide the sensor
kit 100 access to those features. Examples of services that may be
subscribed to include analytics services, AI-services, notification
services, and the like. The dashboard may allow the user to perform
additional or alternative configurations.
[0262] In embodiments, the configuration module 534 maintains
configurations of respective sensor kits 100. Initially, when a new
sensor kit 100 is deployed in an industrial setting 120, the
configuration module 534 may update the sensor kit data store 510
with the device IDs of each device in the newly installed sensor
kit 100. Once the sensor kit data store 510 has updated the sensor
kit data store 510 to reflect the newly installed sensor kit 100,
the backend system 150 may begin storing sensor data from the
sensor kit 100. In embodiments, new sensors 102 may be added to
respective sensor kits 100. In these embodiments, an edge device
104 may provide an add request to the backend system 150 upon an
attempt to add a device to the sensor kit 100. In embodiments, the
request may indicate a sensor ID of the new sensor. In response to
the request, the configuration module 534 may add the sensor ID of
the new sensor to the sensor kit data of the requesting sensor kit
100 in the sensor kit data store 510.
[0263] In embodiments, the backend system 150 includes a
distributed ledger management module 536. In some of these
embodiments, the distributed ledger management module 536 allows a
user to update and/or configure a distributed ledger. In some of
these embodiments, the distributed ledger management module 536
allows a user to define or upload a smart contract. As discussed,
the smart contract may include one or more conditions that are
verified by the smart contract and one or more actions that are
triggered when the conditions are verified. In embodiments, the
user may provide one or more conditions that are to be verified to
the distributed ledger management module 536 via a user interface.
In some of these embodiments, the user may provide the code (e.g.,
JavaScript code, Java code, C code, C++code, etc.) that defines the
conditions. The user may also provide the actions that are to be
performed in response to certain conditions being met. In response
to a smart contract being uploaded/created, the distributed ledger
management module 536 may deploy the smart contract. In
embodiments, the distributed ledger management module 536 may
generate a block containing the smart contract. The block may
include a header that defines an address of the block, and a body
that includes an address to a previous block and the smart
contract. In some embodiments, the distributed ledger management
module 536 may determine a hash value based on the body of the
block and/or may encrypt the block. The distributed ledger
management module 536 may transmit the block to one or more node
computing devices 160, which in turn update the distributed ledger
with the block containing the smart contract. The distributed
ledger management module 536 may further provide the address of the
block to one or more parties that may access the smart contract.
The distributed ledger management module 536 may perform additional
or alternative functions without departing from the scope of the
disclosure.
[0264] The backend system 150 may include additional or alternative
components, data stores, and/or modules that are not discussed.
[0265] FIGS. 6-9--Exemplary Methods of Encoding and/or Decoding
Sensor Data
[0266] FIG. 6 illustrates an example set of operations of a method
600 for compressing sensor data obtained by a sensor kit 100. In
embodiments, the method 600 may be performed by an edge device 104
of a sensor kit 100.
[0267] At 610, the edge device 104 receives sensor data from one or
more sensors 102 of the sensor kit 100 via a sensor kit network
200. In embodiments, the sensor data from a respective sensor 102
may be received in a reporting packet. Each reporting packet may
include a device identifier of the sensor 102 that generated the
reporting packet and one or more instances of sensor data captured
by sensor 102. The reporting packet may include additional data,
such as a timestamp or other metadata.
[0268] At 612, the edge device 104 processes the sensor data. In
embodiments, the edge device 104 may dedupe any reporting packets
that are duplicative. In embodiments, the edge device 104 may
filter out sensor data that is clearly erroneous (e.g., outside of
a tolerance range). In embodiments, the edge device 104 may
aggregate the sensor data obtained from multiple sensors 102. In
embodiments, the edge device 104 may perform one or more AI related
tasks, such as determining a prediction or classification relating
to a condition of one or more industrial components of the
industrial setting 120. In some of these embodiments, the decision
to compress the sensor data may depend on whether the edge device
104 determines that there are any potential issues with the
industrial component. For example, the edge device 104 may compress
the sensor data when there have been no issues predicted or
classified. In other embodiments, the edge device 104 may compress
any sensor data that is being transmitted to the backend system or
certain types of sensor data (e.g., sensor data obtained from
temperature sensors).
[0269] At 614, the edge device 104 may compress the sensor data.
The edge device 104 may employ any suitable compression techniques
for compressing the sensor data. For example, the edge device 104
may employ vertical or horizontal compression techniques. The edge
device 104 may be configured with a codec that compresses the
sensor data. The codec may be a proprietary codec or an
"off-the-shelf" codec.
[0270] At 616, the edge device 104 may transmit the compressed
sensor data to the backend system 150. In embodiments, the edge
device 104 may generate a sensor kit packet that contains the
compressed data. The sensor kit packet may designate the source of
the sensor kit packet (e.g., a sensor kit ID or edge device ID) and
may include additional metadata (e.g., a timestamp). In
embodiments, the edge device 104 may encrypt the sensor kit packet
prior to transmitting the sensor kit packet to the backend system
150. In embodiments, the edge device 104 transmits the sensor kit
packet to the backend system 150 directly (e.g., via a cellular
connection, a network connection, or a satellite uplink). In other
embodiments, the edge device 104 transmits the sensor kit packet to
the backend system 150 via a gateway device, which transmits the
sensor kit packet to the backend system 150 directly (e.g., via a
cellular connection or a satellite uplink).
[0271] FIG. 7 illustrates an example set of operations of a method
700 for processing compressed sensor data received from a sensor
kit 100. In embodiments, the method 700 is executed by a backend
system 150.
[0272] At 710, the backend system 150 receives compressed sensor
data from a sensor kit. In embodiments, the compressed sensor data
may be received in a sensor kit packet.
[0273] At 712, the backend system 150 decompresses the received
sensor data. In embodiments, the backend system may utilize a codec
to decompress the received sensor data. Prior to decompressing the
received sensor data, the backend system 150 may decrypt a sensor
kit packet containing the compressed sensor data.
[0274] At 714, the backend system 150 performs one or more backend
operations on the decompressed sensor data. The backend operations
may include storing the data, filtering the data, performing
AI-related tasks on the sensor data, issuing one or more
notifications in relation to the results of the AI-related tasks,
performing one or more analytics related tasks, controlling an
industrial component of the industrial setting 120, and the
like.
[0275] FIG. 8 illustrates an example set of operations of a method
800 for streaming sensor data from a sensor kit 100 to a backend
system 150. In embodiments, the method 800 may be executed by an
edge device 104 of the sensor kit 100.
[0276] At 810, the edge device 104 receives sensor data from one or
more sensors 102 of the sensor kit 100 via a sensor kit network
200. In embodiments, the sensor data from a respective sensor 102
may be received in a reporting packet. Each reporting packet may
include a device identifier of the sensor 102 that generated the
reporting packet and one or more instances of sensor data captured
by sensor 102. The reporting packet may include additional data,
such as a timestamp or other metadata. In embodiments, the edge
device 104 may process the sensor data. For example, the edge
device 104 may dedupe any reporting packets that are duplicative
and/or may filter out sensor data that is clearly erroneous (e.g.,
outside of a tolerance range). In embodiments, the edge device 104
may aggregate the sensor data obtained from multiple sensors
102.
[0277] At 812, the edge device 104 may normalize and/or transform
the sensor data into a media-frame compliant format. In
embodiments, the edge device 104 may normalize and/or transform
each sensor data instance into a value that adheres to the
restrictions of a media frame that will contain the sensor data.
For example, in embodiments where the media frames are video
frames, the edge device 104 may normalize and/or transform
instances of sensor data into acceptable pixel frames. The edge
device 104 may employ one or more mappings and/or normalization
functions to transform and/or normalize the sensor data.
[0278] At 814, the edge device 104 may generate a block of media
frames based on the transformed and/or normalized sensor data. For
example, in embodiments where the media frames are video frames,
the edge device 104 may populate each instance of transformed
and/or normalized sensor data into a respective pixel of the video
frame. The manner by which the edge device 104 assigns an instance
of transformed and/or normalized sensor data to a respective pixel
may be defined in a mapping that maps respective sensors to
respective pixel values. In embodiments, the mapping may be defined
so as to minimize variance between the values in adjacent pixels.
In embodiments, the edge device 104 may generate a series of
time-sequenced media frames, such that each successive media frame
corresponds to a subsequent set of sensor data instances.
[0279] At 816, the edge device 104 may encode the block of the
media frame. In embodiments, the edge device 104 may employ an
encoder of a media codec (e.g., a video codec) to compress the
block of media frames. The codec may be a proprietary codec or an
"off-the-shelf" codec. For example, the media codec may be an
H.264/MPEG-4 codec, an H.265/MPEG-H codec, an H.263/MPEG-4 codec,
proprietary codecs, and the like. The codec receives the block of
media frames and generates an encoded media block based
thereon.
[0280] At 818, the edge device 104 may transmit the encoded media
block to the backend system 150. In embodiments, the edge device
104 may stream the encoded media blocks to the backend system 150.
Each encoded block may designate the source of the block (e.g., a
sensor kit ID or edge device ID) and may include additional
metadata (e.g., a timestamp and/or a block identifier). In
embodiments, the edge device 104 may encrypt the encoded media
blocks prior to transmitting encoded media blocks to the backend
system 150. The edge device 104 may transmit the encoded media
blocks to the backend system 150 directly (e.g., via a cellular
connection, a network connection, or a satellite uplink) or via a
gateway device, which transmits the encoded media block to the
backend system 150 directly (e.g., via a cellular connection or a
satellite uplink).
[0281] The edge device 104 may continue to execute the foregoing
method 800, so as to deliver a stream of live sensor data from a
sensor kit. The foregoing method 900 may be performed in settings
where there are many sensors deployed within the setting and the
sensors are sampled frequently or continuously. In this way, the
bandwidth required to provide the sensor data to the backend system
is reduced.
[0282] FIG. 9 illustrates an example set of operations of a method
900 for ingesting a sensor data stream from an edge device 104. In
embodiments, the method 900 is executed by a backend system.
[0283] At 910, the backend system 150 receives an encoded media
block from a sensor kit. The backend system 150 may receive encoded
media blocks as part of a sensor data stream.
[0284] At 912, the backend system 150 decodes the encoded block
using a decoder corresponding to the codec of the codec used to
encode the media block to obtain a set of successive media frames.
As discussed with respect to the encoding operation, the codec may
be a proprietary codec or an "off-the-shelf" codec. For example,
the media codec may be an H.264/MPEG-4 codec, an H.265/MPEG-H
codec, an H.263/MPEG-4 codec, proprietary codecs, and the like. The
codec receives the encoded block of media frames and decodes the
encoded block to obtain a set of sequential media frames.
[0285] At 914, the backend system 150 recreates the sensor data
based on the media frame. In embodiments, the backend system 150
determines the normalized and/or transformed sensor values embedded
in each respective media frame. For example, in embodiments where
the media frames are video frames, the backend system 150 may
determine pixel values for each pixel in the media frame. A pixel
value may correspond to respective sensor 102 of a sensor kit 100
and the value may represent a normalized and/transformed instance
of sensor data. In embodiments, the backend system 150 may recreate
the sensor data by inversing the normalization and/or
transformation of the pixel value. In embodiments, the backend
system 150 may utilize an inverse transformation and/or an inverse
normalization function to obtain each recreated sensor data
instance.
[0286] AT 918, the backend system 150 performs one or more backend
operations based on the recreated sensor data. The backend
operations may include storing the data, filtering the data,
performing AI-related tasks on the sensor data, issuing one or more
notifications in relation to the results of the AI-related tasks,
performing one or more analytics related tasks, controlling an
industrial component of the industrial setting 120, and the
like.
[0287] FIG. 10--Exemplary Method of Determining Transmission
Strategy
[0288] FIG. 10 illustrates a set of operations of a method 1000 for
determining a transmission strategy and/or a storage strategy for
sensor data collected by a sensor kit 100 based on the sensor data.
A transmission strategy may define a manner that sensor data is
transmitted (if at all) to the backend system. For example, sensor
data may be compressed using an aggressive lossy codec, compressed
using a lossless codec, and/or transmitted without compression. A
storage strategy may define a manner by which sensor data is stored
at the edge device 104. For example, sensor data may be stored
permanently (or until a human removes the sensor data), may be
stored for a period of time (e.g., one year) or may be discarded.
The method 1000 may be executed by an edge device 104. The method
1000 may be executed to reduce the network bandwidth consumed by
the sensor kit 100 and/or reduce the storage constraints at the
edge device 104.
[0289] At 1010, the edge device 104 receives sensor data from the
sensors 102 of the sensor kit 100. The data may be received
continuously or intermittently. In embodiments, the sensors 102 may
push the sensor data to the edge device 104 and/or the edge device
104 may request the sensor data 102 from the sensors 102
periodically. In embodiments, the edge device 104 may process the
sensor data upon receipt, including deduping the sensor data.
[0290] In embodiments, the edge device 104 may be configured to
perform one or more AI-related tasks prior to transmission via the
satellite uplink. In some of these embodiments, the edge device 104
may be configured to determine whether there are likely no issues
relating to any of the components and/or the industrial setting 120
based on the sensor data and one or more machine-learned
models.
[0291] At 1012, the edge device 104 may generate one or more
feature vectors based on the sensor data. The feature vectors may
include sensor data from a single sensor 102, a subset of sensors
102, or all of the sensors 102 of the sensor kit 100. In scenarios
where a single sensor or a subset of sensors 102 are included in
the feature vector, the machine-learned model may be trained to
identify one or more issues relating to an industrial component or
the industrial setting 120, but may not be sufficient to fully deem
the entire setting as likely safe/free from issues. Additionally or
alternatively, the feature vectors may correspond to a single
snapshot in time (e.g., all sensor data in the feature vector
corresponds to the same sampling event) or over a period of time
(sensor data samples from a most recent sampling event and sensor
data samples from previous sampling events). In embodiments where
the feature vectors define sensor data from a single snapshot, the
machine-learned models may be trained to identify potential issues
without any temporal context. In embodiments where the feature
vectors define sensor data over a period of time, the
machine-learned models may be trained to identify potential issues
with the context of what the sensor(s) 102 was/were reporting
previously. In these embodiments, the edge device 104 may maintain
a cache of sensor data that is sampled over a predetermined time
(e.g., previous hour, previous day, previous N days), such that the
cache is cleared out in a first-in-first-out manner. In these
embodiments, the edge device 104 may retrieve the previous sensor
data samples from the cache to use to generate feature vectors that
have data samples spanning a period of time.
[0292] At 1014, the edge device 104 may input the one or more
feature vectors into one or more respective machine-learned models.
A respective model may output a prediction or classification
relating to an industrial component and/or the industrial setting
120, and a confidence score relating to the prediction or
classification.
[0293] At 1016, the edge device 104 may determine a transmission
strategy and/or a storage strategy based on the output of the
machine-learned models. In some embodiments, the edge device 104
may make determinations relating to the manner by which sensor data
is transmitted to the backend system 150. In some embodiments, the
edge device 104 may make determinations relating to the manner by
which sensor data is transmitted to the backend system 150 and/or
stored at the edge device. In some of these embodiments, the edge
device 104 may compress sensor data when there are no likely issues
across the entire industrial setting 120 and individual components
of the industrial setting 120. For example, if the machine-learned
models predict that there are likely no issues and classify that
there are currently no issues with a high degree of confidence
(e.g., the confidence score is greater than .98), the edge device
104 may compress the sensor data. Alternatively, in the scenario
where the machine-learned models predict that there are likely no
issues and classify that there are currently no issues with a high
degree of confidence, the edge device 104 may forego transmission
but may store the sensor data at the edge device 104 for a
predefined period of time (e.g., a one-year expiry). In scenarios
where a machine-learned model predicts a potential issue or
classifies a current issue, the edge device 104 may transmit the
sensor data without compressing the sensor data or using a lossless
compression codec. Additionally or alternatively, in scenarios
where a machine-learned model predicts a potential issue or
classifies a current issue, the edge device 104 may store the
sensor data used to make the prediction or classification
indefinitely, as well as data that was collected prior to and/or
after the condition was predicted or classified.
[0294] FIGS. 11-15--Exemplary Sensor Kit Configurations
[0295] FIG. 11 illustrates an example configuration of a sensor kit
1100 according to some embodiments of the present disclosure. In
the illustrated example, the sensor kit 1100 is configured to
communicate with a communication network 180 via an uplink 1108 to
a satellite 1110. In embodiments, the sensor kit 1100 of FIG. 11 is
configured for use in industrial setting 120 located in remote
locations, where cellular coverage is unreliable or non-existent.
In embodiments, the sensor kit 1100 may be installed in natural
resource extraction, natural resource transportation systems, power
generation facilities, and the like. For example, the sensor kit
1100 may be deployed in an oil or natural gas fields, off-shore oil
rigs, mines, oil or gas pipelines, solar fields, wind farms,
hydroelectric power stations, and the like.
[0296] In the example of FIG. 11, the server kit 1100 includes an
edge device 104 and a set of sensors 102. The sensors 102 may
include various types of sensors 102, which may vary depending on
the industrial setting 120. In the illustrated example, the sensors
102 communicate with the edge device 104 via a mesh network. In
these embodiments, the sensors 102 may communicate sensor data to
proximate sensors 102, so as to propagate the sensor data to the
edge device 104 located at the remote/peripheral areas of the
industrial setting 120 to the edge device 104. While a mesh network
is shown, the sensor kits 1100 of FIG. 11 may include alternative
network topologies, such as a hierarchal topology (e.g., some or
all of the sensors 102 communicate with the edge device 104 via
respective collection devices) or a star topology (e.g., sensors
102 communicate to the edge device directly).
[0297] In the embodiments of FIG. 11, the edge device 104 includes
a satellite terminal with a directional antenna that communicates
with a satellite. The satellite terminal may be pre-configured to
communicate with a geosynchronous or low Earth orbit satellites.
The edge device 104 may receive sensor data from the sensor kit
network established by the sensor kit 1100. The edge device 104 may
then transmit the sensor data to the backend system 150 via the
satellite 1110.
[0298] In embodiments, the configurations of the server kit 1100
are suited for industrial setting 120 covering a remote area where
external power sources are not abundant. In embodiments, the sensor
kit 1100 may include external power sources, such as batteries,
rechargeable batteries, generators, and/or solar panels. In these
embodiments, the external power sources may be deployed to power
the sensors 102, the edge device 104, and any other devices in the
sensor kit 1100.
[0299] In embodiments, the configurations of the server kit 1100
are suited for outdoor industrial setting 120. In embodiments, the
sensors 102, the edge device 104, and other devices of the sensor
kit 100 (e.g., collection devices) may be configured with
weatherproof housings. In these embodiments, the sensor kit 1100
may be deployed in an outdoor setting.
[0300] In embodiments, the edge device 104 may be configured to
perform one or more AI-related tasks prior to transmission via the
satellite uplink. In some of these embodiments, the edge device 104
may be configured to determine whether there are likely no issues
relating to any of the components and/or the industrial setting 120
based on the sensor data and one or more machine-learned models. In
embodiments, the edge device 104 may receive the sensor data from
the various sensors and may generate one or more feature vectors
based thereon. The feature vectors may include sensor data from a
single sensor 102, a subset of sensors 102, or all of the sensors
102 of the sensor kit 1100. In scenarios where a single sensor or a
subset of sensors 102 are included in the feature vector, the
machine-learned model may be trained to identify one or more issues
relating to an industrial component or the industrial setting 120,
but may not be sufficient to fully deem the entire setting as
likely safe/free from issues. Additionally or alternatively, the
feature vectors may correspond to a single snapshot in time (e.g.,
all sensor data in the feature vector corresponds to the same
sampling event) or over a period of time (sensor data samples from
a most recent sampling event and sensor data samples from previous
sampling events). In embodiments where the feature vectors define
sensor data from a single snapshot, the machine-learned models may
be trained to identify potential issues without any temporal
context. In embodiments where the feature vectors define sensor
data over a period of time, the machine-learned models may be
trained to identify potential issues with the context of what the
sensor(s) 102 was/were reporting previously. In these embodiments,
the edge device 104 may maintain a cache of sensor data that is
sampled over a predetermined time (e.g., previous hour, previous
day, previous N days), such that the cache is cleared out in a
first-in-first-out manner. In these embodiments, the edge device
104 may retrieve the previous sensor data samples from the cache to
use to generate feature vectors that have data samples spanning a
period of time.
[0301] In embodiments, the edge device 104 may feed the one or more
feature vectors into one or more respective machine-learned models.
A respective model may output a prediction or classification
relating to an industrial component and/or the industrial setting
120, and a confidence score relating to the prediction or
classification. In some embodiments, the edge device 104 may make
determinations relating to the manner by which sensor data is
transmitted to the backend system 150 and/or stored at the edge
device. For instance, in some embodiments, the edge device 104 may
compress sensor data based on the prediction or classification. In
some of these embodiments, the edge device 104 may compress sensor
data when there are no likely issues across the entire industrial
setting 120 and individual components of the industrial setting
120. For example, if the machine-learned models predict that there
are likely no issues and classify that there are currently no
issues with a high degree of confidence (e.g., the confidence score
is greater than .98), the edge device 104 may compress the sensor
data. Alternatively, in the scenario where the machine-learned
models predict that there are likely no issues and classify that
there are currently no issues with a high degree of confidence, the
edge device 104 may forego transmission but may store the sensor
data at the edge device 104 for a predefined period of time (e.g.,
one year). In scenarios where a machine-learned model predicts a
potential issue or classifies a current issue, the edge device 104
may transmit the sensor data without compressing the sensor data or
using a lossless compression codec. In this way, the amount of
bandwidth that is transmitted via the satellite uplink may be
reduced, as the majority of the time the sensor data will be
compressed or not transmitted.
[0302] In embodiments, the edge device 104 may apply one or more
rules to determine whether a triggering condition exists. In
embodiments, the one or more rules may be tailored to identify
potentially dangerous and/or emergency situations. In these
embodiments, the edge device 104 may trigger one or more
notifications or alarms when a triggering condition exists.
Additionally or alternatively, the edge device 104 may transmit the
sensor data without any compression when a triggering condition
exists.
[0303] FIG. 12 illustrates an example configuration of a sensor kit
1200 according to some embodiments of the present disclosure. In
the illustrated example, the sensor kit 1200 is configured to
include a gateway device 1206 that communicates with a
communication network 180 via an uplink 1108 to a satellite 1110.
In embodiments, the sensor kit 1200 of FIG. 12 is configured for
use in industrial setting 120 located in remote locations, where
cellular coverage is unreliable or non-existent, and where the edge
device 104 is located in a location where physical transmission to
a satellite is unreliable or impossible. In embodiments, the sensor
kit 1100 may be installed in underground or underwater facilities,
or in facilities having very thick walls. For example, the sensor
kit 1100 may be deployed in underground mines, underwater oil or
gas pipelines, underwater hydroelectric power stations, and the
like.
[0304] In the example of FIG. 12, the server kit 1200 includes an
edge device 104, a set of sensors 102, and a gateway device 1206.
In embodiments, the gateway device 1206 is a communication device
that includes a satellite terminal with a directional antenna that
communicates with a satellite. The satellite terminal may be
pre-configured to communicate with a geosynchronous or low Earth
orbit satellites. In embodiments, the gateway device 1206 may
communicate with the edge device 104 via a wired communication link
1208 (e.g., Ethernet). The edge device 104 may receive sensor data
from the sensor kit network established by the sensor kit 1200. The
edge device 104 may then transmit the sensor data to the gateway
device 1206 via the wired communication link 1208. The gateway
device 1206 may then communicate the sensor data to the backend
system 150 via the satellite uplink 1108.
[0305] The sensors 102 may include various types of sensors 102,
which may vary depending on the industrial setting 120. In the
illustrated example, the sensors 102 communicate with the edge
device 104 via a mesh network. In these embodiments, the sensors
102 may communicate sensor data to proximate sensors 102, so as to
propagate the sensor data to the edge device 104 located at the
remote/peripheral areas of the industrial setting 120 to the edge
device 104. While a mesh network is shown, the sensor kits 1200 of
FIG. 12 may include alternative network topologies, such as a
hierarchal topology (e.g., some or all of the sensors 102
communicate with the edge device 104 via respective collection
devices) or a star topology (e.g., sensors 102 communicate to the
edge device directly).
[0306] In embodiments, the configurations of the server kit 1200
are suited for industrial setting 120 covering a remote area where
external power sources are not abundant. In embodiments, the sensor
kit 1200 may include external power sources, such as batteries,
rechargeable batteries, generators, and/or solar panels. In these
embodiments, the external power sources may be deployed to power
the sensors 102, the edge device 104, and any other devices in the
sensor kit 1200.
[0307] In embodiments, the configurations of the server kit 1200
are suited for underground or underwater industrial setting 120. In
embodiments, the sensors 102, the edge device 104, and other
devices of the sensor kit 100 (e.g., collection devices) may be
configured with waterproof housings or otherwise airtight housings
(to prevent dust from entering the edge device 104 and/or sensor
devices 102). Furthermore, as the gateway device 1208 is likely to
be situated outdoors, the gateway device 1208 may include a
weatherproof housing.
[0308] In embodiments, the edge device 104 may be configured to
perform one or more AI-related tasks prior to transmission via the
satellite uplink. In some of these embodiments, the edge device 104
may be configured to determine whether there are likely no issues
relating to any of the components and/or the industrial setting 120
based on the sensor data and one or more machine-learned models. In
embodiments, the edge device 104 may receive the sensor data from
the various sensors and may generate one or more feature vectors
based thereon. The feature vectors may include sensor data from a
single sensor 102, a subset of sensors 102, or all of the sensors
102 of the sensor kit 1200. In scenarios where a single sensor or a
subset of sensors 102 are included in the feature vector, the
machine-learned model may be trained to identify one or more issues
relating to an industrial component or the industrial setting 120,
but may not be sufficient to fully deem the entire setting as
likely safe/free from issues. Additionally or alternatively, the
feature vectors may correspond to a single snapshot in time (e.g.,
all sensor data in the feature vector corresponds to the same
sampling event) or over a period of time (sensor data samples from
a most recent sampling event and sensor data samples from previous
sampling events). In embodiments where the feature vectors define
sensor data from a single snapshot, the machine-learned models may
be trained to identify potential issues without any temporal
context. In embodiments where the feature vectors define sensor
data over a period of time, the machine-learned models may be
trained to identify potential issues with the context of what the
sensor(s) 102 was/were reporting previously. In these embodiments,
the edge device 104 may maintain a cache of sensor data that is
sampled over a predetermined time (e.g., previous hour, previous
day, previous N days), such that the cache is cleared out in a
first-in-first-out manner. In these embodiments, the edge device
104 may retrieve the previous sensor data samples from the cache to
use to generate feature vectors that have data samples spanning a
period of time.
[0309] In embodiments, the edge device 104 may feed the one or more
feature vectors into one or more respective machine-learned models.
A respective model may output a prediction or classification
relating to an industrial component and/or the industrial setting
120, and a confidence score relating to the prediction or
classification. In some embodiments, the edge device 104 may make
determinations relating to the manner by which sensor data is
transmitted to the backend system 150 and/or stored at the edge
device. For instance, in some embodiments, the edge device 104 may
compress sensor data based on the prediction or classification. In
some of these embodiments, the edge device 104 may compress sensor
data when there are no likely issues across the entire industrial
setting 120 and individual components of the industrial setting
120. For example, if the machine-learned models predict that there
are likely no issues and classify that there are currently no
issues with a high degree of confidence (e.g., a confidence score
is greater than 0.98), the edge device 104 may compress the sensor
data. Alternatively, in the scenario where the machine-learned
models predict that there are likely no issues and classify that
there are currently no issues with a high degree of confidence, the
edge device 104 may forego transmission but may store the sensor
data at the edge device 104 for a predefined period of time (e.g.,
one year). In scenarios where a machine-learned model predicts a
potential issue or classifies a current issue, the edge device 104
may transmit the sensor data without compressing the sensor data or
using a lossless compression codec. In this way, the amount of
bandwidth that is transmitted via the satellite uplink may be
reduced, as the majority of the time the sensor data will be
compressed or not transmitted.
[0310] In embodiments, the edge device 104 may apply one or more
rules to determine whether a triggering condition exists. In
embodiments, the one or more rules may be tailored to identify
potentially dangerous and/or emergency situations. In these
embodiments, the edge device 104 may trigger one or more
notifications or alarms when a triggering condition exists.
Additionally or alternatively, the edge device 104 may transmit the
sensor data (via the gateway device 1206) without any compression
when a triggering condition exists.
[0311] FIG. 13 illustrates an example configuration of a server kit
1300 according to some embodiments of the present disclosure. In
the example of FIG. 13, the server kit 1300 includes an edge device
104, a set of sensors, and a set of collection devices. In
embodiments, the configurations of the server kit 1300 are suited
for industrial setting 120 covering a large area and where power
sources are abundant; but where the industrial operator does not
wish to connect the sensor kit 1400 to the private network of the
industrial setting 120. In embodiments, the edge device 104
includes a cellular communication device (e.g., a 4G LTE chipset or
5G LTE chipset) with a transceiver that communicates with a
cellular tower 1310. The cellular communication may be
pre-configured to communicate with a cellular data provider. For
example, in embodiments, the edge device 104 may include a SIM card
that is registered with a cellular provider having a cellular tower
1310 that is proximate to the industrial setting 120. The edge
device 104 may receive sensor data from the sensor kit network
established by the sensor kit 1400. The edge device 104 may process
the sensor data and then transmit the sensor data to the backend
system 150 via the cellular tower 1310.
[0312] The sensors 102 may include various types of sensors 102,
which may vary depending on the industrial setting 120. In the
illustrated example, the sensors 102 communicate with the edge
device 104 via a hierarchical network. In these embodiments, the
sensors 102 may communicate sensor data to collection devices 206,
which, in turn, may communicate the sensor data to edge device 104
via a wired or wireless communication link. The hierarchical
network may be deployed where the area being monitored is rather
larger (e.g., over 40,000 sq. ft.) and power supplies are abundant,
such as in a factory, a power plant, a food inspection facility, an
indoor grow facility, and the like. While a hierarchal network is
shown, the sensor kits 1300 of FIG. 13 may include alternative
network topologies, such as a mesh topology or a star topology
(e.g., sensors 102 communicate to the edge device directly).
[0313] In embodiments, the edge device 104 may be configured to
perform one or more AI-related tasks prior to transmission via the
satellite uplink. In some of these embodiments, the edge device 104
may be configured to determine whether there are likely no issues
relating to any of the components and/or the industrial setting 120
based on the sensor data and one or more machine-learned models. In
embodiments, the edge device 104 may receive the sensor data from
the various sensors and may generate one or more feature vectors
based thereon. The feature vectors may include sensor data from a
single sensor 102, a subset of sensors 102, or all of the sensors
102 of the sensor kit 1300. In scenarios where a single sensor or a
subset of sensors 102 are included in the feature vector, the
machine-learned model may be trained to identify one or more issues
relating to an industrial component or the industrial setting 120,
but may not be sufficient to fully deem the entire setting as
likely safe/free from issues. Additionally or alternatively, the
feature vectors may correspond to a single snapshot in time (e.g.,
all sensor data in the feature vector corresponds to the same
sampling event) or over a period of time (sensor data samples from
a most recent sampling event and sensor data samples from previous
sampling events). In embodiments where the feature vectors define
sensor data from a single snap shot, the machine-learned models may
be trained to identify potential issues without any temporal
context. In embodiments where the feature vectors define sensor
data over a period of time, the machine-learned models may be
trained to identify potential issues with the context of what the
sensor(s) 102 was/were reporting previously. In these embodiments,
the edge device 104 may maintain a cache of sensor data that is
sampled over a predetermined time (e.g., previous hour, previous
day, previous N days), such that the cache is cleared out in a
first-in-first-out manner. In these embodiments, the edge device
104 may retrieve the previous sensor data samples from the cache to
use to generate feature vectors that have data samples spanning a
period of time.
[0314] In embodiments, the edge device 104 may feed the one or more
feature vectors into one or more respective machine-learned models.
A respective model may output a prediction or classification
relating to an industrial component and/or the industrial setting
120, and a confidence score relating to the prediction or
classification. In some embodiments, the edge device 104 may make
determinations relating to the manner by which sensor data is
transmitted to the backend system 150 and/or stored at the edge
device. For instance, in some embodiments, the edge device 104 may
compress sensor data based on the prediction or classification. In
some of these embodiments, the edge device 104 may compress sensor
data when there are no likely issues across the entire industrial
setting 120 and individual components of the industrial setting
120. For example, if the machine-learned models predict that there
are likely no issues and classify that there are currently no
issues with a high degree of confidence (e.g., a confidence score
is greater than 0.98), the edge device 104 may compress the sensor
data. Alternatively, in the scenario where the machine-learned
models predict that there are likely no issues and classify that
there are currently no issues with a high degree of confidence, the
edge device 104 may forego transmission but may store the sensor
data at the edge device 104 for a predefined period of time (e.g.,
one year). In scenarios where a machine-learned model predicts a
potential issue or classifies a current issue, the edge device 104
may transmit the sensor data without compressing the sensor data or
using a lossless compression codec. In this way, the amount of
bandwidth that is transmitted via the cellular tower may be
reduced, as the majority of the time the sensor data will be
compressed or not transmitted.
[0315] In embodiments, the edge device 104 may apply one or more
rules to determine whether a triggering condition exists. In
embodiments, the one or more rules may be tailored to identify
potentially dangerous and/or emergency situations. In these
embodiments, the edge device 104 may trigger one or more
notifications or alarms when a triggering condition exists.
Additionally or alternatively, the edge device 104 may transmit the
sensor data without any compression when a triggering condition
exists.
[0316] FIG. 14 illustrates an example configuration of a server kit
1400 according to some embodiments of the present disclosure. In
the example of FIG. 14, the server kit 1400 includes an edge device
104, a set of sensors 102, a set of collection devices 206, and a
gateway device 1406. In embodiments, the configurations of the
server kit 1400 are suited for industrial setting 120 covering a
large area and where power sources are abundant; but where the
industrial operator does not wish to connect the sensor kit 1400 to
the private network of the industrial setting 120 and the walls of
the industrial setting 120 make wireless communication (e.g.,
cellular communication) unreliable or impossible. In embodiments,
the gateway device 1406 is a cellular network gateway device that
includes a cellular communication device (e.g., 4G, 5G chipset)
with a transceiver that communicates with a cellular tower 1310.
The cellular communication may be pre-configured to communicate
with a cellular data provider. For example, in embodiments, the
gateway device may include a SIM card that is registered with a
cellular provider having a tower 1310 that is proximate to the
industrial setting 120. In embodiments, the gateway device 1406 may
communicate with the edge device 104 via a wired communication link
1408 (e.g., Ethernet). The edge device 104 may receive sensor data
from the sensor kit network established by the sensor kit 1400. The
edge device 104 may then transmit the sensor data to the gateway
device 1406 via the wired communication link 1408. The gateway
device 1406 may then communicate the sensor data to the backend
system 150 via the cellular tower 1310.
[0317] The sensors 102 may include various types of sensors 102,
which may vary depending on the industrial setting 120. In the
illustrated example, the sensors 102 communicate with the edge
device 104 via a hierarchical network. In these embodiments, the
sensors 102 may communicate sensor data to collection devices 206,
which, in turn, may communicate the sensor data to edge device 104
via a wired or wireless communication link. The hierarchical
network may be deployed where the area being monitored is rather
larger (e.g., over 40,000 sq. ft.) and power supplies are abundant,
such as in a factory, a power plant, a food inspection facility, an
indoor grow facility, and the like. While a hierarchal network is
shown, the sensor kits 1400 of FIG. 14 may include alternative
network topologies, such as a mesh topology or a star topology
(e.g., sensors 102 communicate to the edge device directly).
[0318] In embodiments, the edge device 104 may be configured to
perform one or more AI-related tasks prior to transmission via the
satellite uplink. In some of these embodiments, the edge device 104
may be configured to determine whether there are likely no issues
relating to any of the components and/or the industrial setting 120
based on the sensor data and one or more machine-learned models. In
embodiments, the edge device 104 may receive the sensor data from
the various sensors and may generate one or more feature vectors
based thereon. The feature vectors may include sensor data from a
single sensor 102, a subset of sensors 102, or all of the sensors
102 of the sensor kit 1400. In scenarios where a single sensor or a
subset of sensors 102 are included in the feature vector, the
machine-learned model may be trained to identify one or more issues
relating to an industrial component or the industrial setting 120,
but may not be sufficient to fully deem the entire setting as
likely safe/free from issues. Additionally or alternatively, the
feature vectors may correspond to a single snapshot in time (e.g.,
all sensor data in the feature vector corresponds to the same
sampling event) or over a period of time (sensor data samples from
a most recent sampling event and sensor data samples from previous
sampling events). In embodiments where the feature vectors define
sensor data from a single snapshot, the machine-learned models may
be trained to identify potential issues without any temporal
context. In embodiments where the feature vectors define sensor
data over a period of time, the machine-learned models may be
trained to identify potential issues with the context of what the
sensor(s) 102 was/were reporting previously. In these embodiments,
the edge device 104 may maintain a cache of sensor data that is
sampled over a predetermined time (e.g., previous hour, previous
day, previous N days), such that the cache is cleared out in a
first-in-first-out manner. In these embodiments, the edge device
104 may retrieve the previous sensor data samples from the cache to
use to generate feature vectors that have data samples spanning a
period of time.
[0319] In embodiments, the edge device 104 may feed the one or more
feature vectors into one or more respective machine-learned models.
A respective model may output a prediction or classification
relating to an industrial component and/or the industrial setting
120, and a confidence score relating to the prediction or
classification. In some embodiments, the edge device 104 may make
determinations relating to the manner by which sensor data is
transmitted to the backend system 150 and/or stored at the edge
device. For instance, in some embodiments, the edge device 104 may
compress sensor data based on the prediction or classification. In
some of these embodiments, the edge device 104 may compress sensor
data when there are no likely issues across the entire industrial
setting 120 and individual components of the industrial setting
120. For example, if the machine-learned models predict that there
are likely no issues and classify that there are currently no
issues with a high degree of confidence (e.g., the confidence score
is greater than .98), the edge device 104 may compress the sensor
data. Alternatively, in the scenario where the machine-learned
models predict that there are likely no issues and classify that
there are currently no issues with a high degree of confidence, the
edge device 104 may forego transmission but may store the sensor
data at the edge device 104 for a predefined period of time (e.g.,
one year). In scenarios where a machine-learned model predicts a
potential issue or classifies a current issue, the edge device 104
may transmit the sensor data without compressing the sensor data or
using a lossless compression codec. In this way, the amount of
bandwidth that is transmitted via the cellular tower may be
reduced, as the majority of the time the sensor data will be
compressed or not transmitted.
[0320] In embodiments, the edge device 104 may apply one or more
rules to determine whether a triggering condition exists. In
embodiments, the one or more rules may be tailored to identify
potentially dangerous and/or emergency situations. In these
embodiments, the edge device 104 may trigger one or more
notifications or alarms when a triggering condition exists.
Additionally or alternatively, the edge device 104 may transmit the
sensor data without any compression when a triggering condition
exists.
[0321] FIG. 15 illustrates an example configuration of a server kit
1500 for installation in an agricultural setting 1520 according to
some embodiments of the present disclosure. In the example of FIG.
15, the server kit 1500 is configured for installation in an indoor
agricultural setting 1520 that may include, but is not limited to,
a control system 1522, an HVAC system 1524, a lighting system 1526,
a power system 1528, and/or an irrigation system 1530. In this
example, various features and components of the agricultural
setting include components that are monitored by a set of sensors
102. In embodiments, the sensors 102 capture instances of sensor
data and provide the respective instances of sensor data to an edge
device 104. In the example embodiments of FIG. 15, the sensor kit
1500 includes a set of collection devices 206 that route sensor
data from the sensors 102 to the edge device 104. Sensor kits 1500
for deployment in agricultural settings may have different sensor
kit network topologies as well. For instance, in facilities not
having more than two or three rooms being monitored, the sensor kit
network may be a mesh or star network, depending on the distances
between the edge device 104 and the furthest potential sensor
location. For example, if the distance between the edge device 104
and the furthest potential sensor location is greater than 150
meters, then the sensor kit network may be configured as a mesh
network. In the embodiments of FIG. 15, the edge device 104
transmits the sensor data to the backend system 150 directly. In
these embodiments, the edge device 104 includes a cellular
communication device that communicates with a cellular tower 1310
of a preset cellular provider via a preconfigured cellular
connection to a cellular tower 1310. In other embodiments of the
disclosure, the edge device 104 transmits the sensor data to the
backend system 150 via a gateway device (e.g., gateway device 1406)
that includes a cellular communication device that communicates
with a cellular tower 1310 of a preset cellular provider.
[0322] In embodiments, a server kit 1500 may include any suitable
combination of light sensors 1502, weight sensors 1504, temperature
sensors 1506, CO2 sensors 1508, humidity sensors 1510, fan speed
sensors 1512, and/or audio/visual (AV) sensors 1514 (e.g.,
cameras). Sensor kits 1500 may be arranged with additional or
alternative sensors 102. In embodiments, the sensor data collected
by the edge device 104 may include ambient light measurements
indicating an amount of ambient light detected in the area of a
light sensor 1502. In embodiments, the sensor data collected by the
edge device 104 may include a weight or mass measurements
indicating a weight or mass of an object (e.g., a pot or tray
containing one or more plants) that is resting upon a weight sensor
1504. In embodiments, the sensor data collected by the edge device
104 may include temperature measurements indicating an ambient
temperature in the vicinity of a temperature sensor 1506. In
embodiments, the sensor data collected by the edge device 104 may
include humidity measurements indicating an ambient humidity in the
vicinity of a humidity sensor 1510 or moisture measurements
indicating a relative amount of moisture in a medium (e.g., soil)
monitored by a humidity sensor 1510. In embodiments, the sensor
data collected by the edge device 104 may include CO2 measurements
indicating ambient levels of CO2 in the vicinity of a CO2 sensor
1508. In embodiments, the sensor data collected by the edge device
104 may include temperature measurements indicating an ambient
temperature in the vicinity of a temperature sensor 1506. In
embodiments, the sensor data collected by the edge device 104 may
include fan speed measurements indicating a measured speed of a fan
(e.g., a fan of an HVAC system 1524) as measured by a fan speed
sensor 1512. In embodiments, the sensor data collected by the edge
device 104 may include video signals captured by an AV sensor 1516.
The sensor data captured by sensors 102 and collected by the edge
device 104 may include additional or alternative types of sensor
data without departing from the scope of the disclosure.
[0323] In embodiments, the edge device 104 is configured to perform
one or more edge operations on the sensor data. For example, the
edge device 104 may pre-process the received sensor data. In
embodiments, the edge device 104 may predict or classify potential
issues with one or more components of the HVAC system 1524,
lighting system 1526, power system 1528, the irrigation system
1530; the plants growing in the agricultural facility; and/or the
facility itself. In embodiments, the edge device 104 may analyze
the sensor data with respect to a set of rules that define
triggering conditions. In these embodiments, the edge device 104
may trigger alarms or notifications in response to a triggering
condition being met. In embodiments, the edge device 104 may
encode, compress, and/or encrypt the sensor data, prior to
transmission to the backend system 150. In some of these
embodiments, the edge device 104 may selectively compress the
sensor data based on predictions or classifications made by the
edge device 104 and/or upon one or more triggering conditions being
met.
[0324] In embodiments, the edge device 104 may be configured to
perform one or more AI-related tasks prior to transmission via the
satellite uplink. In some of these embodiments, the edge device 104
may be configured to determine whether there are likely no issues
relating to any of the components and/or the industrial setting 120
based on the sensor data and one or more machine-learned models. In
embodiments, the edge device 104 may receive the sensor data from
the various sensors and may generate one or more feature vectors
based thereon. The feature vectors may include sensor data from a
single sensor 102, a subset of sensors 102, or all of the sensors
102 of the sensor kit 1300. In scenarios where a single sensor or a
subset of sensors 102 are included in the feature vector, the
machine-learned model may be trained to identify one or more issues
relating to an industrial component or the industrial setting 120,
but may not be sufficient to fully deem the entire setting as
likely safe/free from issues. Additionally or alternatively, the
feature vectors may correspond to a single snapshot in time (e.g.,
all sensor data in the feature vector corresponds to the same
sampling event) or over a period of time (sensor data samples from
a most recent sampling event and sensor data samples from previous
sampling events). In embodiments where the feature vectors define
sensor data from a single snapshot, the machine-learned models may
be trained to identify potential issues without any temporal
context. In embodiments where the feature vectors define sensor
data over a period of time, the machine-learned models may be
trained to identify potential issues with the context of what the
sensor(s) 102 was/were reporting previously. In these embodiments,
the edge device 104 may maintain a cache of sensor data that is
sampled over a predetermined time (e.g., previous hour, previous
day, previous N days), such that the cache is cleared out in a
first-in-first-out manner. In these embodiments, the edge device
104 may retrieve the previous sensor data samples from the cache to
use to generate feature vectors that have data samples spanning a
period of time.
[0325] In embodiments, the edge device 104 may feed the one or more
feature vectors into one or more respective machine-learned models.
A respective model may output a prediction or classification
relating to an industrial component and/or the industrial setting
120, and a confidence score relating to the prediction or
classification. In some embodiments, the edge device 104 may make
determinations relating to the manner by which sensor data is
transmitted to the backend system 150 and/or stored at the edge
device. For instance, in some embodiments, the edge device 104 may
compress sensor data based on the prediction or classification. In
some of these embodiments, the edge device 104 may compress sensor
data when there are no likely issues across the entire industrial
setting 120 and individual components of the industrial setting
120. For example, if the machine-learned models predict that there
are likely no issues and classify that there are currently no
issues with a high degree of confidence (e.g., the confidence score
is greater than .98), the edge device 104 may compress the sensor
data. Alternatively, in the scenario where the machine-learned
models predict that there are likely no issues and classify that
there are currently no issues with a high degree of confidence, the
edge device 104 may forego transmission but may store the sensor
data at the edge device 104 for a predefined period of time (e.g.,
one year). In scenarios where a machine-learned model predicts a
potential issue or classifies a current issue, the edge device 104
may transmit the sensor data without compressing the sensor data or
using a lossless compression codec. In this way, the amount of
bandwidth that is transmitted via the cellular tower may be
reduced, as the majority of the time the sensor data will be
compressed or not transmitted.
[0326] In embodiments, the edge device 104 may apply one or more
rules to the sensor data to determine whether a triggering
condition exists. In embodiments, the one or more rules may be
tailored to identify potentially dangerous and/or emergency
situations. In these embodiments, the edge device 104 may trigger
one or more notifications or alarms when a triggering condition
exists. Additionally or alternatively, the edge device 104 may
transmit the sensor data without any compression when a triggering
condition exists. In some embodiments, the edge device 104 may
selectively compress and/or transmit the sensor data based on the
application of the one or more rules to the sensor data.
[0327] In embodiments, the backend system 150 may perform one or
more backend operations based on received sensor data. In
embodiments, the backend system 150 may decode/decompress/decrypt
the sensor data received from respective sensor kits 1500. In
embodiments, the backend system 150 may preprocess received sensor
data. In embodiments, the backend system 150 may preprocess sensor
data received from a respective server kit 1500. For example, the
backend system 150 may filter, dedupe, and/or structure the sensor
data. In embodiments, the backend system 150 may perform one or
more AI-related tasks using the sensor data. In some of these
embodiments, the backend system 150 may extract features from the
sensor data, which may be used to predict on classify certain
conditions or events relating to the agricultural setting. For
example, the backend system 150 may deploy models used to predict
yields of a crop based on weight measurements, temperature
measurements, CO2 measurements, light measurements, and/or other
extracted features. In another example, the backend system 150 may
deploy models used to predict or classify mold-inducing states in a
room or area of the agricultural facility based on temperature
measurements, humidity measurements, video signals or images,
and/or other extracted features. In embodiments, the backend system
150 may perform one or more analytics tasks on the sensor data and
may display the results to a human user via a dashboard. In some
embodiments, the backend system 150 may receive control commands
from a human user via the dashboard. For example, a human resource
with sufficient login credentials may control an HVAC system 1524,
a lighting system 1526, a power system 1528, and/or an irrigation
system 1530 of the industrial setting 120. In some of these
embodiments, the backend system 150 may telemetrically monitor the
actions of the human user, and may train one or more
machine-learned models (e.g., neural networks) on actions to take
in response to displaying the analytics results to the human user.
In other embodiments, the backend system 150 may execute one or
more workflows associated with the HVAC system 1524, the lighting
system 1526, the power system 1528, and/or the irrigation system
1530, in order to control one or more of the systems of the
agricultural setting 1520 based on a prediction or classification
made by the backend system in response to the sensor data. In
embodiments, the backend system 150 provides one or more control
commands to a control system 1522 of an agricultural setting 1520,
which in turn may control the HVAC system 1524, the lighting system
1526, the power system 1528, and/or the irrigation system 1530
based on the received control commands. In embodiments, the backend
system 150 may provide or utilize an API to provide control
commands to the agricultural setting 1520.
[0328] FIG. 16--Exemplary Method of Monitoring Industrial
Settings
[0329] FIG. 16 illustrates an example set of operations of a method
1600 for monitoring industrial setting 120 using an automatically
configured backend system 150. In embodiments, the method 1600 may
be performed by the backend system 150, the sensor kit 100, and the
dashboard module 532.
[0330] At 1602, the backend system 150 registers the sensor kit 100
to a respective industrial setting 120. In some embodiments, the
backend system 150 registers a plurality of sensor kits 100 and
registers each sensor kit 100 of the plurality of sensor kits 100
to a respective industrial setting 120. In embodiments, the backend
system 150 provides an interface for specifying a type of entity or
industrial setting 120 to be monitored. In some embodiments, a user
may select a set of parameters for monitoring of the respective
industrial setting 120 of the sensor kit 100. The backend system
150 may automatically provision a set of services and capabilities
of the backend system 150 based on the selected parameters.
[0331] At 1604, the backend system 150 configures the sensor kit
100 to monitor physical characteristics of the respective
industrial setting 120 to which the sensor kit 100 is registered.
For example, when the respective industrial setting 120 is a
natural resource extraction setting, the backend system 150 may
configure one or more of infrared sensors, ground penetrating
sensors, light sensors, humidity sensors, temperature sensors,
chemical sensors, fan speed sensors, rotational speed sensors,
weight sensors, and camera sensors to monitor and collect sensor
data relating to metrics and parameters of the natural resource
extraction setting and equipment used therein.
[0332] At 1606, the sensor kit 100 transmits instances of sensor
data to the backend system 150. In some embodiments, the sensor kit
100 transmits the instances of sensor data to the backend system
150 via a gateway device. The gateway device may provide a virtual
container for instances of the sensor data such that only a
registered owner or operator of the respective industrial setting
120 can access the sensor data via the backend system 150.
[0333] At 1608, the backend system 150 processes instances of
sensor data received from the sensor kit 100. In some embodiments,
the backend system 150 includes an analytics facility and/or a
machine learning facility. The analytics facility and/or the
machine learning facility may be configured based on the type of
the industrial setting 120 and may process the instances of sensor
data received from the sensor kit 100. In some embodiments, the
backend system 150 updates and/or configures a distributed ledger
based on the processed instances of sensor data.
[0334] At 1610, the backend system 150 configures and populates the
dashboard. In embodiments, the backend system 150 configures the
dashboard to retrieve and display one or more of raw sensor data
provided by the sensor kit, analytical data relating to the sensor
data provided by the sensor kit 100, predictions or classifications
made by the backend system 150 based on the sensor data, and the
like. In some embodiments, the backend system 150 configures alarm
limits with respect to one or more sensor types and/or conditions
based on the industrial setting 120. The backend system 150 may
define which users receive a notification when an alarm is
triggered. In embodiments, the backend system 150 may subscribe to
additional features of the backend system 150 and/or an edge device
104 based on the industrial setting 120.
[0335] At 1612, the dashboard provides monitoring information to a
human user. In embodiments, the dashboard provides monitoring
information to the user by displaying the monitoring information on
a device, e.g., a computer terminal, a smartphone, a monitor, or
any other suitable device for displaying information. The
monitoring information may be provided via a graphical user
interface.
[0336] FIG. 17 illustrates an exemplary manufacturing facility 1700
according to some embodiments of the present disclosure. The
manufacturing facility 1700 may include a plurality of industrial
machines 1702 including, by way of example, conveyor belts,
assembly machines, die machines, turbines, and power systems. The
manufacturing facility 1700 may further include a plurality of
products 1704. The manufacturing facility may have the sensor kit
100 installed therein, the sensor kit 100 including the plurality
of sensors 102 and the edge device 104. By way of example, one or
more of the sensors 102 may be installed on some or all of the
industrial machines 1702 and the products 1704.
[0337] FIG. 18 illustrates a surface portion of an exemplary
underwater industrial facility 1800 according to some embodiments
of the present disclosure. The underwater industrial facility 1800
may include a transportation and communication platform 1802, a
storage platform 1804, and a pumping platform 1806. The underwater
industrial facility 1800 may have the sensor kit 100 installed
therein, the sensor kit 100 including the plurality of sensors 102
and the edge device 104. By way of example, one or more of the
sensors 102 may be installed on some or all of the transportation
and communication platform 1802, the storage platform 1804, and the
pumping platform 1806, and on individual components and machines
thereof
[0338] FIG. 19 illustrates an exemplary indoor agricultural
facility 1900 according to some embodiments of the present
disclosure. The indoor agricultural facility 1900 may include a
greenhouse 1902 and a plurality of wind turbines 1904. The indoor
agricultural facility 1900 may have the sensor kit 100 installed
therein, the sensor kit 100 including the plurality of sensors 102
and the edge device 104. By way of example, one or more of the
sensors 102 may be installed on some or all components of the
greenhouse 1904 and on some or all components of the wind turbines
1904.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having the second communication device of the edge device is a
satellite terminal device that is configured to transmit the sensor
kit packets to a satellite that routes the sensor kits to the
public network. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security including a gateway device that is
configured to receive sensor kit packets from the edge device via a
wired communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having the edge device further includes one or more storage
devices that store a sensor data store that stores instances of
sensor data captured by the plurality of sensors of the sensor kit.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having the self-configuring sensor kit network is a star
network such that each sensor of the plurality of sensors transmits
respective instances of sensor data with the edge device directly
using a short-range communication protocol. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
including a gateway device that is configured to receive sensor kit
packets from the edge device via a wired communication link and
transmit the sensor kit packets to the backend system via the
public network on behalf of the edge device and having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having sensors and an edge device that stores multiple models
and performs AI-related tasks based on sensor data obtained from
the sensor using an appropriate model. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having sensors and an edge device
that compresses sensor data collected by the sensor using a media
codec. In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having a sensor kit and a backend system configured to receive
sensor data collected by the sensor kit and perform one or more
backend operations on the sensor data. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having sensors and an edge device
that are configured to monitor an indoor agricultural setting. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having sensors and an edge device that are configured to
monitor a natural resource extraction setting. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
including a gateway device that is configured to receive sensor kit
packets from the edge device via a wired communication link and
transmit the sensor kit packets to the backend system via the
public network on behalf of the edge device and having sensors and
an edge device that are configured to monitor a pipeline setting.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having sensors and an edge device that are configured to
monitor a manufacturing facility. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having sensors and an edge device
that are configured to monitor an underwater industrial setting. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having a sensor kit that collects sensor data and a backend
system that receives the sensor data from the sensor kits and
updates a distributed ledger based on the sensor data. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having sensors and an edge device that is configured to add new
sensors to the sensor kit. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having sensors, an edge device,
and a gateway device that communicates with a communication network
on behalf of the sensor kit. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having an edge device that
includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having an edge device that
includes an encoding module that encodes, compresses, and/or
encrypts sensor data according to one or more media codecs. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having an edge device that includes a quick-decision AI module
that uses machine-learned models to generate predictions related to
and/or classifications of industrial components based on features
of collected sensor data. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having an edge device that
includes a notification module that provides notifications and/or
alarms to users based on sensor data and/or rules applied to the
sensor data. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security including a gateway device that is
configured to receive sensor kit packets from the edge device via a
wired communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having an edge device that includes a configuration module that
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security including a gateway device that is
configured to receive sensor kit packets from the edge device via a
wired communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having an edge device that includes a distributed ledger module
configured to update a distributed ledger with sensor data captured
by the sensor kit. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security including a gateway device that is
configured to receive sensor kit packets from the edge device via a
wired communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having a backend system that includes a decoding module that
decrypts, decodes, and/or decompresses encoded sensor kit packets.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having a backend system that includes a data processing module
that executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a
gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having a backend system that
includes an AI module that trains machine-learned models to make
predictions or classifications related to sensor data captured by a
sensor kit. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security including a gateway device that is
configured to receive sensor kit packets from the edge device via a
wired communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having a backend system that includes a notification module
that issues notifications to users when an issue is detected in an
industrial setting based on collected sensor data. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
including a gateway device that is configured to receive sensor kit
packets from the edge device via a wired communication link and
transmit the sensor kit packets to the backend system via the
public network on behalf of the edge device and having a backend
system that includes an analytics module that performs analytics
tasks on sensor data received from the sensor kit. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
including a gateway device that is configured to receive sensor kit
packets from the edge device via a wired communication link and
transmit the sensor kit packets to the backend system via the
public network on behalf of the edge device and having a backend
system that includes a control module that provides commands to a
device or system in an industrial setting to take remedial action
in response to a particular issue being detected. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
including a gateway device that is configured to receive sensor kit
packets from the edge device via a wired communication link and
transmit the sensor kit packets to the backend system via the
public network on behalf of the edge device and having a backend
system that includes a dashboard module that presents a dashboard
to a human user that provides the human user with raw sensor data,
analytical data, and/or predictions or classifications based on
sensor data received from the sensor kit. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having a backend system that
includes a dashboard module that presents a dashboard to a human
user that provides a graphical user interface that allows the user
to configure the sensor kit system. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having a sensor kit and a backend
system that includes a configuration module that maintains
configurations of the sensor kit and configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having a sensor kit and a backend system that updates a smart
contract defining a condition that may trigger an action based on
sensor data received from the sensor kit. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security including
a gateway device that is configured to receive sensor kit packets
from the edge device via a wired communication link and transmit
the sensor kit packets to the backend system via the public network
on behalf of the edge device and having a distributed ledger that
is at least partially shared with a regulatory body to provide
information related to compliance with a regulation or regulatory
action. In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security including a gateway device that is configured to
receive sensor kit packets from the edge device via a wired
communication link and transmit the sensor kit packets to the
backend system via the public network on behalf of the edge device
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
including a gateway device that is configured to receive sensor kit
packets from the edge device via a wired communication link and
transmit the sensor kit packets to the backend system via the
public network on behalf of the edge device and having a sensor, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the second communication
device of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network and having the
self-configuring sensor kit network is a star network such that
each sensor of the plurality of sensors transmits respective
instances of sensor data with the edge device directly using a
short-range communication protocol. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having sensors in a self-configuring network and an
edge device that performs one or more backend operations on sensor
data obtained from the sensor. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having sensors and an edge device that stores multiple
models and performs AI-related tasks based on sensor data obtained
from the sensor using an appropriate model. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the second communication device of the edge device is a
satellite terminal device that is configured to transmit the sensor
kit packets to a satellite that routes the sensor kits to the
public network and having sensors and an edge device that
compresses sensor data collected by the sensor using a media codec.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having a sensor kit and a
backend system configured to receive sensor data collected by the
sensor kit and perform one or more backend operations on the sensor
data. In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having sensors and an edge
device that are configured to monitor an indoor agricultural
setting. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the second communication
device of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network and having sensors and
an edge device that are configured to monitor a natural resource
extraction setting. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the second communication
device of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network and having sensors and
an edge device that are configured to monitor a pipeline setting.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having sensors and an edge
device that are configured to monitor a manufacturing facility. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having sensors and an edge
device that are configured to monitor an underwater industrial
setting. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the second communication
device of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network and having a sensor
kit that collects sensor data and a backend system that receives
the sensor data from the sensor kits and updates a distributed
ledger based on the sensor data. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having sensors and an edge device that is configured to
add new sensors to the sensor kit. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having sensors, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the second communication
device of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network and having an edge
device that includes a data processing module that deduplicates,
filters, flags, and/or aggregates sensor data. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the second communication device of the edge device is a
satellite terminal device that is configured to transmit the sensor
kit packets to a satellite that routes the sensor kits to the
public network and having an edge device that includes an encoding
module that encodes, compresses, and/or encrypts sensor data
according to one or more media codecs. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having an edge device that includes a quick-decision AI
module that uses machine-learned models to generate predictions
related to and/or classifications of industrial components based on
features of collected sensor data. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having an edge device that includes a notification
module that provides notifications and/or alarms to users based on
sensor data and/or rules applied to the sensor data. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having an edge device that
includes a configuration module that configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having an edge device that
includes a distributed ledger module configured to update a
distributed ledger with sensor data captured by the sensor kit. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having a backend system that
includes a decoding module that decrypts, decodes, and/or
decompresses encoded sensor kit packets. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having a backend system that includes a data processing
module that executes a workflow associated with a potential issue
based on sensor data captured by the sensor kit. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the second communication device of the edge device is a
satellite terminal device that is configured to transmit the sensor
kit packets to a satellite that routes the sensor kits to the
public network and having a backend system that includes an AI
module that trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having a backend system that
includes a notification module that issues notifications to users
when an issue is detected in an industrial setting based on
collected sensor data. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring
industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the second communication
device of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network and having a backend
system that includes an analytics module that performs analytics
tasks on sensor data received from the sensor kit. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the second communication device of the edge device is a
satellite terminal device that is configured to transmit the sensor
kit packets to a satellite that routes the sensor kits to the
public network and having a backend system that includes a control
module that provides commands to a device or system in an
industrial setting to take remedial action in response to a
particular issue being detected. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides the
human user with raw sensor data, analytical data, and/or
predictions or classifications based on sensor data received from
the sensor kit. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the second communication
device of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network and having a backend
system that includes a dashboard module that presents a dashboard
to a human user that provides a graphical user interface that
allows the user to configure the sensor kit system. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the second communication device of the edge device is a
satellite terminal device that is configured to transmit the sensor
kit packets to a satellite that routes the sensor kits to the
public network and having a sensor kit and a backend system that
includes a configuration module that maintains configurations of
the sensor kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the second communication device of the edge
device is a satellite terminal device that is configured to
transmit the sensor kit packets to a satellite that routes the
sensor kits to the public network and having a sensor kit and a
backend system that updates a smart contract defining a condition
that may trigger an action based on sensor data received from the
sensor kit. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the second communication
device of the edge device is a satellite terminal device that is
configured to transmit the sensor kit packets to a satellite that
routes the sensor kits to the public network and having a
distributed ledger that is at least partially shared with a
regulatory body to provide information related to compliance with a
regulation or regulatory action. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
second communication device of the edge device is a satellite
terminal device that is configured to transmit the sensor kit
packets to a satellite that routes the sensor kits to the public
network and having sensor kit and a backend system that updates a
smart contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the second communication device of the edge device is a
satellite terminal device that is configured to transmit the sensor
kit packets to a satellite that routes the sensor kits to the
public network and having a sensor, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having sensors in a self-configuring
network and an edge device that performs one or more backend
operations on sensor data obtained from the sensor. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the edge device further includes one or more storage devices
that store a sensor data store that stores instances of sensor data
captured by the plurality of sensors of the sensor kit and having
sensors and an edge device that stores multiple models and performs
AI-related tasks based on sensor data obtained from the sensor
using an appropriate model. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
edge device further includes one or more storage devices that store
a sensor data store that stores instances of sensor data captured
by the plurality of sensors of the sensor kit and having sensors
and an edge device that compresses sensor data collected by the
sensor using a media codec. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
edge device further includes one or more storage devices that store
a sensor data store that stores instances of sensor data captured
by the plurality of sensors of the sensor kit and having a sensor
kit and a backend system configured to receive sensor data
collected by the sensor kit and perform one or more backend
operations on the sensor data. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
edge device further includes one or more storage devices that store
a sensor data store that stores instances of sensor data captured
by the plurality of sensors of the sensor kit and having sensors
and an edge device that are configured to monitor an indoor
agricultural setting. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having sensors and an edge device
that are configured to monitor a natural resource extraction
setting. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having sensors and an edge device
that are configured to monitor a pipeline setting. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the edge device further includes one or more storage devices
that store a sensor data store that stores instances of sensor data
captured by the plurality of sensors of the sensor kit and having
sensors and an edge device that are configured to monitor a
manufacturing facility. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having sensors and an edge device
that are configured to monitor an underwater industrial setting. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit and having a sensor kit that collects sensor data
and a backend system that receives the sensor data from the sensor
kits and updates a distributed ledger based on the sensor data. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit and having sensors and an edge device that is
configured to add new sensors to the sensor kit. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the edge device further includes one or more storage devices
that store a sensor data store that stores instances of sensor data
captured by the plurality of sensors of the sensor kit and having
sensors, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit and having an edge device that includes a data
processing module that deduplicates, filters, flags, and/or
aggregates sensor data. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having an edge device that includes
an encoding module that encodes, compresses, and/or encrypts sensor
data according to one or more media codecs. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the edge device further includes one or more storage devices
that store a sensor data store that stores instances of sensor data
captured by the plurality of sensors of the sensor kit and having
an edge device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit and having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the edge device further includes one or more storage devices
that store a sensor data store that stores instances of sensor data
captured by the plurality of sensors of the sensor kit and having
an edge device that includes a distributed ledger module configured
to update a distributed ledger with sensor data captured by the
sensor kit. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having a backend system that includes
a decoding module that decrypts, decodes, and/or decompresses
encoded sensor kit packets. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
edge device further includes one or more storage devices that store
a sensor data store that stores instances of sensor data captured
by the plurality of sensors of the sensor kit and having a backend
system that includes a data processing module that executes a
workflow associated with a potential issue based on sensor data
captured by the sensor kit. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
edge device further includes one or more storage devices that store
a sensor data store that stores instances of sensor data captured
by the plurality of sensors of the sensor kit and having a backend
system that includes an AI module that trains machine-learned
models to make predictions or classifications related to sensor
data captured by a sensor kit. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
edge device further includes one or more storage devices that store
a sensor data store that stores instances of sensor data captured
by the plurality of sensors of the sensor kit and having a backend
system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. In embodiments, provided
herein are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
edge device further includes one or more storage devices that store
a sensor data store that stores instances of sensor data captured
by the plurality of sensors of the sensor kit and having a backend
system that includes an analytics module that performs analytics
tasks on sensor data received from the sensor kit. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the edge device further includes one or more storage devices
that store a sensor data store that stores instances of sensor data
captured by the plurality of sensors of the sensor kit and having a
backend system that includes a control module that provides
commands to a device or system in an industrial setting to take
remedial action in response to a particular issue being detected.
In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having a backend system that includes
a dashboard module that presents a dashboard to a human user that
provides the human user with raw sensor data, analytical data,
and/or predictions or classifications based on sensor data received
from the sensor kit. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having a backend system that includes
a dashboard module that presents a dashboard to a human user that
provides a graphical user interface that allows the user to
configure the sensor kit system. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
edge device further includes one or more storage devices that store
a sensor data store that stores instances of sensor data captured
by the plurality of sensors of the sensor kit and having a sensor
kit and a backend system that includes a configuration module that
maintains configurations of the sensor kit and configures a sensor
kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit and having a sensor kit and a backend system that
updates a distributed ledger based on sensor data provided by the
sensor kit. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the edge device further
includes one or more storage devices that store a sensor data store
that stores instances of sensor data captured by the plurality of
sensors of the sensor kit and having a sensor kit and a backend
system that updates a smart contract defining a condition that may
trigger an action based on sensor data received from the sensor
kit. In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit and having a distributed ledger that is at least
partially shared with a regulatory body to provide information
related to compliance with a regulation or regulatory action. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the edge device further includes one or more
storage devices that store a sensor data store that stores
instances of sensor data captured by the plurality of sensors of
the sensor kit and having sensor kit and a backend system that
updates a smart contract, wherein the smart contract verifies one
or more conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the edge device further includes one or more storage devices
that store a sensor data store that stores instances of sensor data
captured by the plurality of sensors of the sensor kit and having a
sensor, an edge device, and a gateway device that communicates with
a communication network on behalf of the sensor kit.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol and having
sensors in a self-configuring network and an edge device that
performs one or more backend operations on sensor data obtained
from the sensor. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having sensors and an edge device that stores multiple
models and performs AI-related tasks based on sensor data obtained
from the sensor using an appropriate model. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the self-configuring sensor kit network is a star network
such that each sensor of the plurality of sensors transmits
respective instances of sensor data with the edge device directly
using a short-range communication protocol and having sensors and
an edge device that compresses sensor data collected by the sensor
using a media codec. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having a sensor kit and a backend system configured to
receive sensor data collected by the sensor kit and perform one or
more backend operations on the sensor data. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the self-configuring sensor kit network is a star network
such that each sensor of the plurality of sensors transmits
respective instances of sensor data with the edge device directly
using a short-range communication protocol and having sensors and
an edge device that are configured to monitor an indoor
agricultural setting. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having sensors and an edge device that are configured
to monitor a natural resource extraction setting. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the self-configuring sensor kit network is a star network
such that each sensor of the plurality of sensors transmits
respective instances of sensor data with the edge device directly
using a short-range communication protocol and having sensors and
an edge device that are configured to monitor a pipeline setting.
In embodiments, provided herein are methods and systems for
monitoring industrial settings, including through a variety of kits
that provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol and having
sensors and an edge device that are configured to monitor a
manufacturing facility. In embodiments, provided herein are methods
and systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having sensors and an edge device that are configured
to monitor an underwater industrial setting. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the self-configuring sensor kit network is a star network
such that each sensor of the plurality of sensors transmits
respective instances of sensor data with the edge device directly
using a short-range communication protocol and having a sensor kit
that collects sensor data and a backend system that receives the
sensor data from the sensor kits and updates a distributed ledger
based on the sensor data. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
self-configuring sensor kit network is a star network such that
each sensor of the plurality of sensors transmits respective
instances of sensor data with the edge device directly using a
short-range communication protocol and having sensors and an edge
device that is configured to add new sensors to the sensor kit. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol and having
sensors, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol and having an
edge device that includes a data processing module that
deduplicates, filters, flags, and/or aggregates sensor data. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol and having an
edge device that includes an encoding module that encodes,
compresses, and/or encrypts sensor data according to one or more
media codecs. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having an edge device that includes a quick-decision
AI module that uses machine-learned models to generate predictions
related to and/or classifications of industrial components based on
features of collected sensor data. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
self-configuring sensor kit network is a star network such that
each sensor of the plurality of sensors transmits respective
instances of sensor data with the edge device directly using a
short-range communication protocol and having an edge device that
includes a notification module that provides notifications and/or
alarms to users based on sensor data and/or rules applied to the
sensor data. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having an edge device that includes a configuration
module that configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
self-configuring sensor kit network is a star network such that
each sensor of the plurality of sensors transmits respective
instances of sensor data with the edge device directly using a
short-range communication protocol and having an edge device that
includes a distributed ledger module configured to update a
distributed ledger with sensor data captured by the sensor kit. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol and having a
backend system that includes a decoding module that decrypts,
decodes, and/or decompresses encoded sensor kit packets. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol and having a
backend system that includes a data processing module that executes
a workflow associated with a potential issue based on sensor data
captured by the sensor kit. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
self-configuring sensor kit network is a star network such that
each sensor of the plurality of sensors transmits respective
instances of sensor data with the edge device directly using a
short-range communication protocol and having a backend system that
includes an AI module that trains machine-learned models to make
predictions or classifications related to sensor data captured by a
sensor kit. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having a backend system that includes a notification
module that issues notifications to users when an issue is detected
in an industrial setting based on collected sensor data. In
embodiments, provided herein are methods and systems for monitoring
industrial settings, including through a variety of kits that
provide out-of-the-box, self-configuring and automatically
provisioned capabilities for monitoring industrial settings while
mitigating issues of complexity, integration, bandwidth, latency
and security having the self-configuring sensor kit network is a
star network such that each sensor of the plurality of sensors
transmits respective instances of sensor data with the edge device
directly using a short-range communication protocol and having a
backend system that includes an analytics module that performs
analytics tasks on sensor data received from the sensor kit. In
embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having a backend system that includes a control module
that provides commands to a device or system in an industrial
setting to take remedial action in response to a particular issue
being detected. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides the
human user with raw sensor data, analytical data, and/or
predictions or classifications based on sensor data received from
the sensor kit. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides a
graphical user interface that allows the user to configure the
sensor kit system. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having a sensor kit and a backend system that includes
a configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein are
methods and systems for monitoring industrial settings, including
through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
self-configuring sensor kit network is a star network such that
each sensor of the plurality of sensors transmits respective
instances of sensor data with the edge device directly using a
short-range communication protocol and having a sensor kit and a
backend system that updates a distributed ledger based on sensor
data provided by the sensor kit. In embodiments, provided herein
are methods and systems for monitoring industrial settings,
including through a variety of kits that provide out-of-the-box,
self-configuring and automatically provisioned capabilities for
monitoring industrial settings while mitigating issues of
complexity, integration, bandwidth, latency and security having the
self-configuring sensor kit network is a star network such that
each sensor of the plurality of sensors transmits respective
instances of sensor data with the edge device directly using a
short-range communication protocol and having a sensor kit and a
backend system that updates a smart contract defining a condition
that may trigger an action based on sensor data received from the
sensor kit. In embodiments, provided herein are methods and systems
for monitoring industrial settings, including through a variety of
kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having a distributed ledger that is at least partially
shared with a regulatory body to provide information related to
compliance with a regulation or regulatory action. In embodiments,
provided herein are methods and systems for monitoring industrial
settings, including through a variety of kits that provide
out-of-the-box, self-configuring and automatically provisioned
capabilities for monitoring industrial settings while mitigating
issues of complexity, integration, bandwidth, latency and security
having the self-configuring sensor kit network is a star network
such that each sensor of the plurality of sensors transmits
respective instances of sensor data with the edge device directly
using a short-range communication protocol and having sensor kit
and a backend system that updates a smart contract, wherein the
smart contract verifies one or more conditions put forth by a
regulatory body with respect to compliance with a regulation or
regulatory action. In embodiments, provided herein are methods and
systems for monitoring industrial settings, including through a
variety of kits that provide out-of-the-box, self-configuring and
automatically provisioned capabilities for monitoring industrial
settings while mitigating issues of complexity, integration,
bandwidth, latency and security having the self-configuring sensor
kit network is a star network such that each sensor of the
plurality of sensors transmits respective instances of sensor data
with the edge device directly using a short-range communication
protocol and having a sensor, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit.
[0343] In embodiments, provided herein is a sensor kit having
sensors in a self-configuring network and an edge device that
performs one or more backend operations on sensor data obtained
from the sensor. In embodiments, provided herein is a sensor kit
having sensors in a self-configuring network and an edge device
that performs one or more backend operations on sensor data
obtained from the sensor and having sensors and an edge device that
stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model. In
embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having sensors and an edge device that compresses sensor data
collected by the sensor using a media codec. In embodiments,
provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having a sensor kit and a backend system configured to receive
sensor data collected by the sensor kit and perform one or more
backend operations on the sensor data. In embodiments, provided
herein is a sensor kit having sensors in a self-configuring network
and an edge device that performs one or more backend operations on
sensor data obtained from the sensor and having sensors and an edge
device that are configured to monitor an indoor agricultural
setting. In embodiments, provided herein is a sensor kit having
sensors in a self-configuring network and an edge device that
performs one or more backend operations on sensor data obtained
from the sensor and having sensors and an edge device that are
configured to monitor a natural resource extraction setting. In
embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having sensors and an edge device that are configured to monitor a
pipeline setting. In embodiments, provided herein is a sensor kit
having sensors in a self-configuring network and an edge device
that performs one or more backend operations on sensor data
obtained from the sensor and having sensors and an edge device that
are configured to monitor a manufacturing facility. In embodiments,
provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having sensors and an edge device that are configured to monitor an
underwater industrial setting. In embodiments, provided herein is a
sensor kit having sensors in a self-configuring network and an edge
device that performs one or more backend operations on sensor data
obtained from the sensor and having a sensor kit that collects
sensor data and a backend system that receives the sensor data from
the sensor kits and updates a distributed ledger based on the
sensor data. In embodiments, provided herein is a sensor kit having
sensors in a self-configuring network and an edge device that
performs one or more backend operations on sensor data obtained
from the sensor and having sensors and an edge device that is
configured to add new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having sensors, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
in a self-configuring network and an edge device that performs one
or more backend operations on sensor data obtained from the sensor
and having an edge device that includes a data processing module
that deduplicates, filters, flags, and/or aggregates sensor data.
In embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having an edge device that includes an encoding module that
encodes, compresses, and/or encrypts sensor data according to one
or more media codecs. In embodiments, provided herein is a sensor
kit having sensors in a self-configuring network and an edge device
that performs one or more backend operations on sensor data
obtained from the sensor and having an edge device that includes a
quick-decision AI module that uses machine-learned models to
generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having an edge device that includes a notification module that
provides notifications and/or alarms to users based on sensor data
and/or rules applied to the sensor data. In embodiments, provided
herein is a sensor kit having sensors in a self-configuring network
and an edge device that performs one or more backend operations on
sensor data obtained from the sensor and having an edge device that
includes a configuration module that configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit. In
embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having an edge device that includes a distributed ledger module
configured to update a distributed ledger with sensor data captured
by the sensor kit. In embodiments, provided herein is a sensor kit
having sensors in a self-configuring network and an edge device
that performs one or more backend operations on sensor data
obtained from the sensor and having a backend system that includes
a decoding module that decrypts, decodes, and/or decompresses
encoded sensor kit packets. In embodiments, provided herein is a
sensor kit having sensors in a self-configuring network and an edge
device that performs one or more backend operations on sensor data
obtained from the sensor and having a backend system that includes
a data processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having a backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having a backend system that includes a notification module that
issues notifications to users when an issue is detected in an
industrial setting based on collected sensor data. In embodiments,
provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having a backend system that includes an analytics module that
performs analytics tasks on sensor data received from the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
in a self-configuring network and an edge device that performs one
or more backend operations on sensor data obtained from the sensor
and having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected. In embodiments, provided herein is a sensor kit having
sensors in a self-configuring network and an edge device that
performs one or more backend operations on sensor data obtained
from the sensor and having a backend system that includes a
dashboard module that presents a dashboard to a human user that
provides the human user with raw sensor data, analytical data,
and/or predictions or classifications based on sensor data received
from the sensor kit. In embodiments, provided herein is a sensor
kit having sensors in a self-configuring network and an edge device
that performs one or more backend operations on sensor data
obtained from the sensor and having a backend system that includes
a dashboard module that presents a dashboard to a human user that
provides a graphical user interface that allows the user to
configure the sensor kit system. In embodiments, provided herein is
a sensor kit having sensors in a self-configuring network and an
edge device that performs one or more backend operations on sensor
data obtained from the sensor and having a sensor kit and a backend
system that includes a configuration module that maintains
configurations of the sensor kit and configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit. In
embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having a sensor kit and a backend system that updates a distributed
ledger based on sensor data provided by the sensor kit. In
embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having a sensor kit and a backend system that updates a smart
contract defining a condition that may trigger an action based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having sensors in a self-configuring network
and an edge device that performs one or more backend operations on
sensor data obtained from the sensor and having a distributed
ledger that is at least partially shared with a regulatory body to
provide information related to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
having sensors in a self-configuring network and an edge device
that performs one or more backend operations on sensor data
obtained from the sensor and having sensor kit and a backend system
that updates a smart contract, wherein the smart contract verifies
one or more conditions put forth by a regulatory body with respect
to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit having sensors in a
self-configuring network and an edge device that performs one or
more backend operations on sensor data obtained from the sensor and
having a sensor, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit.
[0344] In embodiments, provided herein is a sensor kit having
sensors and an edge device that stores multiple models and performs
AI-related tasks based on sensor data obtained from the sensor
using an appropriate model. In embodiments, provided herein is a
sensor kit having sensors and an edge device that stores multiple
models and performs AI-related tasks based on sensor data obtained
from the sensor using an appropriate model and having sensors and
an edge device that compresses sensor data collected by the sensor
using a media codec. In embodiments, provided herein is a sensor
kit having sensors and an edge device that stores multiple models
and performs AI-related tasks based on sensor data obtained from
the sensor using an appropriate model and having a sensor kit and a
backend system configured to receive sensor data collected by the
sensor kit and perform one or more backend operations on the sensor
data. In embodiments, provided herein is a sensor kit having
sensors and an edge device that stores multiple models and performs
AI-related tasks based on sensor data obtained from the sensor
using an appropriate model and having sensors and an edge device
that are configured to monitor an indoor agricultural setting. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that stores multiple models and performs AI-related
tasks based on sensor data obtained from the sensor using an
appropriate model and having sensors and an edge device that are
configured to monitor a natural resource extraction setting. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that stores multiple models and performs AI-related
tasks based on sensor data obtained from the sensor using an
appropriate model and having sensors and an edge device that are
configured to monitor a pipeline setting. In embodiments, provided
herein is a sensor kit having sensors and an edge device that
stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model and
having sensors and an edge device that are configured to monitor a
manufacturing facility. In embodiments, provided herein is a sensor
kit having sensors and an edge device that stores multiple models
and performs AI-related tasks based on sensor data obtained from
the sensor using an appropriate model and having sensors and an
edge device that are configured to monitor an underwater industrial
setting. In embodiments, provided herein is a sensor kit having
sensors and an edge device that stores multiple models and performs
AI-related tasks based on sensor data obtained from the sensor
using an appropriate model and having a sensor kit that collects
sensor data and a backend system that receives the sensor data from
the sensor kits and updates a distributed ledger based on the
sensor data. In embodiments, provided herein is a sensor kit having
sensors and an edge device that stores multiple models and performs
AI-related tasks based on sensor data obtained from the sensor
using an appropriate model and having sensors and an edge device
that is configured to add new sensors to the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that stores multiple models and performs AI-related
tasks based on sensor data obtained from the sensor using an
appropriate model and having sensors, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that stores multiple models and
performs AI-related tasks based on sensor data obtained from the
sensor using an appropriate model and having an edge device that
includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data. In embodiments, provided
herein is a sensor kit having sensors and an edge device that
stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model and
having an edge device that includes an encoding module that
encodes, compresses, and/or encrypts sensor data according to one
or more media codecs. In embodiments, provided herein is a sensor
kit having sensors and an edge device that stores multiple models
and performs AI-related tasks based on sensor data obtained from
the sensor using an appropriate model and having an edge device
that includes a quick-decision AI module that uses machine-learned
models to generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that stores multiple models and performs AI-related
tasks based on sensor data obtained from the sensor using an
appropriate model and having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that stores multiple models and performs AI-related
tasks based on sensor data obtained from the sensor using an
appropriate model and having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model and
having an edge device that includes a distributed ledger module
configured to update a distributed ledger with sensor data captured
by the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that stores multiple models and
performs AI-related tasks based on sensor data obtained from the
sensor using an appropriate model and having a backend system that
includes a decoding module that decrypts, decodes, and/or
decompresses encoded sensor kit packets. In embodiments, provided
herein is a sensor kit having sensors and an edge device that
stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model and
having a backend system that includes a data processing module that
executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit. In embodiments, provided
herein is a sensor kit having sensors and an edge device that
stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model and
having a backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model and
having a backend system that includes a notification module that
issues notifications to users when an issue is detected in an
industrial setting based on collected sensor data. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model and
having a backend system that includes an analytics module that
performs analytics tasks on sensor data received from the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
and an edge device that stores multiple models and performs
AI-related tasks based on sensor data obtained from the sensor
using an appropriate model and having a backend system that
includes a control module that provides commands to a device or
system in an industrial setting to take remedial action in response
to a particular issue being detected. In embodiments, provided
herein is a sensor kit having sensors and an edge device that
stores multiple models and performs AI-related tasks based on
sensor data obtained from the sensor using an appropriate model and
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides the human user
with raw sensor data, analytical data, and/or predictions or
classifications based on sensor data received from the sensor kit.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that stores multiple models and performs AI-related
tasks based on sensor data obtained from the sensor using an
appropriate model and having a backend system that includes a
dashboard module that presents a dashboard to a human user that
provides a graphical user interface that allows the user to
configure the sensor kit system. In embodiments, provided herein is
a sensor kit having sensors and an edge device that stores multiple
models and performs AI-related tasks based on sensor data obtained
from the sensor using an appropriate model and having a sensor kit
and a backend system that includes a configuration module that
maintains configurations of the sensor kit and configures a sensor
kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
and an edge device that stores multiple models and performs
AI-related tasks based on sensor data obtained from the sensor
using an appropriate model and having a sensor kit and a backend
system that updates a distributed ledger based on sensor data
provided by the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that stores multiple
models and performs AI-related tasks based on sensor data obtained
from the sensor using an appropriate model and having a sensor kit
and a backend system that updates a smart contract defining a
condition that may trigger an action based on sensor data received
from the sensor kit. In embodiments, provided herein is a sensor
kit having sensors and an edge device that stores multiple models
and performs AI-related tasks based on sensor data obtained from
the sensor using an appropriate model and having a distributed
ledger that is at least partially shared with a regulatory body to
provide information related to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
having sensors and an edge device that stores multiple models and
performs AI-related tasks based on sensor data obtained from the
sensor using an appropriate model and having sensor kit and a
backend system that updates a smart contract, wherein the smart
contract verifies one or more conditions put forth by a regulatory
body with respect to compliance with a regulation or regulatory
action. In embodiments, provided herein is a sensor kit having
sensors and an edge device that stores multiple models and performs
AI-related tasks based on sensor data obtained from the sensor
using an appropriate model and having a sensor, an edge device, and
a gateway device that communicates with a communication network on
behalf of the sensor kit.
[0345] In embodiments, provided herein is a sensor kit having
sensors and an edge device that compresses sensor data collected by
the sensor using a media codec. In embodiments, provided herein is
a sensor kit having sensors and an edge device that compresses
sensor data collected by the sensor using a media codec and having
a sensor kit and a backend system configured to receive sensor data
collected by the sensor kit and perform one or more backend
operations on the sensor data. In embodiments, provided herein is a
sensor kit having sensors and an edge device that compresses sensor
data collected by the sensor using a media codec and having sensors
and an edge device that are configured to monitor an indoor
agricultural setting. In embodiments, provided herein is a sensor
kit having sensors and an edge device that compresses sensor data
collected by the sensor using a media codec and having sensors and
an edge device that are configured to monitor a natural resource
extraction setting. In embodiments, provided herein is a sensor kit
having sensors and an edge device that compresses sensor data
collected by the sensor using a media codec and having sensors and
an edge device that are configured to monitor a pipeline setting.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that compresses sensor data collected by the sensor
using a media codec and having sensors and an edge device that are
configured to monitor a manufacturing facility. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that compresses sensor data collected by the sensor using a media
codec and having sensors and an edge device that are configured to
monitor an underwater industrial setting. In embodiments, provided
herein is a sensor kit having sensors and an edge device that
compresses sensor data collected by the sensor using a media codec
and having a sensor kit that collects sensor data and a backend
system that receives the sensor data from the sensor kits and
updates a distributed ledger based on the sensor data. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that compresses sensor data collected by the sensor
using a media codec and having sensors and an edge device that is
configured to add new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that compresses sensor data collected by the sensor using a media
codec and having sensors, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
and an edge device that compresses sensor data collected by the
sensor using a media codec and having an edge device that includes
a data processing module that deduplicates, filters, flags, and/or
aggregates sensor data. In embodiments, provided herein is a sensor
kit having sensors and an edge device that compresses sensor data
collected by the sensor using a media codec and having an edge
device that includes an encoding module that encodes, compresses,
and/or encrypts sensor data according to one or more media codecs.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that compresses sensor data collected by the sensor
using a media codec and having an edge device that includes a
quick-decision AI module that uses machine-learned models to
generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that compresses sensor data collected by the sensor
using a media codec and having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that compresses sensor data collected by the sensor
using a media codec and having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that compresses sensor data collected by the sensor using a media
codec and having an edge device that includes a distributed ledger
module configured to update a distributed ledger with sensor data
captured by the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that compresses sensor
data collected by the sensor using a media codec and having a
backend system that includes a decoding module that decrypts,
decodes, and/or decompresses encoded sensor kit packets. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that compresses sensor data collected by the sensor
using a media codec and having a backend system that includes a
data processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that compresses sensor data collected by the sensor
using a media codec and having a backend system that includes an AI
module that trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that compresses sensor data collected by the sensor
using a media codec and having a backend system that includes a
notification module that issues notifications to users when an
issue is detected in an industrial setting based on collected
sensor data. In embodiments, provided herein is a sensor kit having
sensors and an edge device that compresses sensor data collected by
the sensor using a media codec and having a backend system that
includes an analytics module that performs analytics tasks on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having sensors and an edge device that
compresses sensor data collected by the sensor using a media codec
and having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected. In embodiments, provided herein is a sensor kit having
sensors and an edge device that compresses sensor data collected by
the sensor using a media codec and having a backend system that
includes a dashboard module that presents a dashboard to a human
user that provides the human user with raw sensor data, analytical
data, and/or predictions or classifications based on sensor data
received from the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that compresses sensor
data collected by the sensor using a media codec and having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides a graphical user interface
that allows the user to configure the sensor kit system. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that compresses sensor data collected by the sensor
using a media codec and having a sensor kit and a backend system
that includes a configuration module that maintains configurations
of the sensor kit and configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that compresses sensor data collected by the sensor using a media
codec and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that compresses sensor data collected by the sensor
using a media codec and having a sensor kit and a backend system
that updates a smart contract defining a condition that may trigger
an action based on sensor data received from the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that compresses sensor data collected by the sensor
using a media codec and having a distributed ledger that is at
least partially shared with a regulatory body to provide
information related to compliance with a regulation or regulatory
action. In embodiments, provided herein is a sensor kit having
sensors and an edge device that compresses sensor data collected by
the sensor using a media codec and having sensor kit and a backend
system that updates a smart contract, wherein the smart contract
verifies one or more conditions put forth by a regulatory body with
respect to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that compresses sensor data collected by the sensor
using a media codec and having a sensor, an edge device, and a
gateway device that communicates with a communication network on
behalf of the sensor kit.
[0346] In embodiments, provided herein is a sensor kit system
having a sensor kit and a backend system configured to receive
sensor data collected by the sensor kit and perform one or more
backend operations on the sensor data. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having sensors and an edge device that are configured to
monitor an indoor agricultural setting. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having sensors and an edge device that are configured to
monitor a natural resource extraction setting. In embodiments,
provided herein is a sensor kit system having a sensor kit and a
backend system configured to receive sensor data collected by the
sensor kit and perform one or more backend operations on the sensor
data and having sensors and an edge device that are configured to
monitor a pipeline setting. In embodiments, provided herein is a
sensor kit system having a sensor kit and a backend system
configured to receive sensor data collected by the sensor kit and
perform one or more backend operations on the sensor data and
having sensors and an edge device that are configured to monitor a
manufacturing facility. In embodiments, provided herein is a sensor
kit system having a sensor kit and a backend system configured to
receive sensor data collected by the sensor kit and perform one or
more backend operations on the sensor data and having sensors and
an edge device that are configured to monitor an underwater
industrial setting. In embodiments, provided herein is a sensor kit
system having a sensor kit and a backend system configured to
receive sensor data collected by the sensor kit and perform one or
more backend operations on the sensor data and having a sensor kit
that collects sensor data and a backend system that receives the
sensor data from the sensor kits and updates a distributed ledger
based on the sensor data. In embodiments, provided herein is a
sensor kit system having a sensor kit and a backend system
configured to receive sensor data collected by the sensor kit and
perform one or more backend operations on the sensor data and
having sensors and an edge device that is configured to add new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit system having a sensor kit and a backend system
configured to receive sensor data collected by the sensor kit and
perform one or more backend operations on the sensor data and
having sensors, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit. In embodiments, provided herein is a sensor kit system having
a sensor kit and a backend system configured to receive sensor data
collected by the sensor kit and perform one or more backend
operations on the sensor data and having an edge device that
includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having an edge device that includes an encoding module that
encodes, compresses, and/or encrypts sensor data according to one
or more media codecs. In embodiments, provided herein is a sensor
kit system having a sensor kit and a backend system configured to
receive sensor data collected by the sensor kit and perform one or
more backend operations on the sensor data and having an edge
device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data. In embodiments, provided herein is a sensor
kit system having a sensor kit and a backend system configured to
receive sensor data collected by the sensor kit and perform one or
more backend operations on the sensor data and having an edge
device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data. In embodiments, provided herein
is a sensor kit system having a sensor kit and a backend system
configured to receive sensor data collected by the sensor kit and
perform one or more backend operations on the sensor data and
having an edge device that includes a configuration module that
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein is a sensor kit
system having a sensor kit and a backend system configured to
receive sensor data collected by the sensor kit and perform one or
more backend operations on the sensor data and having an edge
device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit. In embodiments, provided herein is a sensor kit system having
a sensor kit and a backend system configured to receive sensor data
collected by the sensor kit and perform one or more backend
operations on the sensor data and having a backend system that
includes a decoding module that decrypts, decodes, and/or
decompresses encoded sensor kit packets. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having a backend system that includes a data processing module
that executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having a backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit system having a sensor kit and a
backend system configured to receive sensor data collected by the
sensor kit and perform one or more backend operations on the sensor
data and having a backend system that includes a notification
module that issues notifications to users when an issue is detected
in an industrial setting based on collected sensor data. In
embodiments, provided herein is a sensor kit system having a sensor
kit and a backend system configured to receive sensor data
collected by the sensor kit and perform one or more backend
operations on the sensor data and having a backend system that
includes an analytics module that performs analytics tasks on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected. In embodiments, provided herein is a sensor kit system
having a sensor kit and a backend system configured to receive
sensor data collected by the sensor kit and perform one or more
backend operations on the sensor data and having a backend system
that includes a dashboard module that presents a dashboard to a
human user that provides the human user with raw sensor data,
analytical data, and/or predictions or classifications based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system.
In embodiments, provided herein is a sensor kit system having a
sensor kit and a backend system configured to receive sensor data
collected by the sensor kit and perform one or more backend
operations on the sensor data and having a sensor kit and a backend
system that includes a configuration module that maintains
configurations of the sensor kit and configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit. In
embodiments, provided herein is a sensor kit system having a sensor
kit and a backend system configured to receive sensor data
collected by the sensor kit and perform one or more backend
operations on the sensor data and having a sensor kit and a backend
system that updates a distributed ledger based on sensor data
provided by the sensor kit. In embodiments, provided herein is a
sensor kit system having a sensor kit and a backend system
configured to receive sensor data collected by the sensor kit and
perform one or more backend operations on the sensor data and
having a sensor kit and a backend system that updates a smart
contract defining a condition that may trigger an action based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having a distributed ledger that is at least partially shared
with a regulatory body to provide information related to compliance
with a regulation or regulatory action. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system configured to receive sensor data collected by the sensor
kit and perform one or more backend operations on the sensor data
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a sensor kit and a
backend system configured to receive sensor data collected by the
sensor kit and perform one or more backend operations on the sensor
data and having a sensor, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit.
[0347] In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor an indoor
agricultural setting. In embodiments, provided herein is a sensor
kit having sensors and an edge device that are configured to
monitor an indoor agricultural setting and having sensors and an
edge device that are configured to monitor a natural resource
extraction setting. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor an
indoor agricultural setting and having sensors and an edge device
that are configured to monitor a pipeline setting. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor an indoor agricultural setting and
having sensors and an edge device that are configured to monitor a
manufacturing facility. In embodiments, provided herein is a sensor
kit having sensors and an edge device that are configured to
monitor an indoor agricultural setting and having sensors and an
edge device that are configured to monitor an underwater industrial
setting. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor an indoor
agricultural setting and having a sensor kit that collects sensor
data and a backend system that receives the sensor data from the
sensor kits and updates a distributed ledger based on the sensor
data. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor an indoor
agricultural setting and having sensors and an edge device that is
configured to add new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor an indoor agricultural setting and
having sensors, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
and an edge device that are configured to monitor an indoor
agricultural setting and having an edge device that includes a data
processing module that deduplicates, filters, flags, and/or
aggregates sensor data. In embodiments, provided herein is a sensor
kit having sensors and an edge device that are configured to
monitor an indoor agricultural setting and having an edge device
that includes an encoding module that encodes, compresses, and/or
encrypts sensor data according to one or more media codecs. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor an indoor agricultural
setting and having an edge device that includes a quick-decision AI
module that uses machine-learned models to generate predictions
related to and/or classifications of industrial components based on
features of collected sensor data. In embodiments, provided herein
is a sensor kit having sensors and an edge device that are
configured to monitor an indoor agricultural setting and having an
edge device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data. In embodiments, provided herein
is a sensor kit having sensors and an edge device that are
configured to monitor an indoor agricultural setting and having an
edge device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
and an edge device that are configured to monitor an indoor
agricultural setting and having an edge device that includes a
distributed ledger module configured to update a distributed ledger
with sensor data captured by the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor an indoor agricultural setting and
having a backend system that includes a decoding module that
decrypts, decodes, and/or decompresses encoded sensor kit packets.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor an indoor
agricultural setting and having a backend system that includes a
data processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor an indoor agricultural
setting and having a backend system that includes an AI module that
trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor an indoor agricultural
setting and having a backend system that includes a notification
module that issues notifications to users when an issue is detected
in an industrial setting based on collected sensor data. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor an indoor agricultural
setting and having a backend system that includes an analytics
module that performs analytics tasks on sensor data received from
the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor an
indoor agricultural setting and having a backend system that
includes a control module that provides commands to a device or
system in an industrial setting to take remedial action in response
to a particular issue being detected. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor an indoor agricultural setting and having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides the human user with raw
sensor data, analytical data, and/or predictions or classifications
based on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor an indoor agricultural setting and
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor an indoor
agricultural setting and having a sensor kit and a backend system
that includes a configuration module that maintains configurations
of the sensor kit and configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor an indoor agricultural setting and
having a sensor kit and a backend system that updates a distributed
ledger based on sensor data provided by the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor an indoor agricultural
setting and having a sensor kit and a backend system that updates a
smart contract defining a condition that may trigger an action
based on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor an indoor agricultural setting and
having a distributed ledger that is at least partially shared with
a regulatory body to provide information related to compliance with
a regulation or regulatory action. In embodiments, provided herein
is a sensor kit having sensors and an edge device that are
configured to monitor an indoor agricultural setting and having
sensor kit and a backend system that updates a smart contract,
wherein the smart contract verifies one or more conditions put
forth by a regulatory body with respect to compliance with a
regulation or regulatory action. In embodiments, provided herein is
a sensor kit having sensors and an edge device that are configured
to monitor an indoor agricultural setting and having a sensor, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit.
[0348] In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a natural
resource extraction setting. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a natural resource extraction setting and having sensors
and an edge device that are configured to monitor a pipeline
setting. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a natural
resource extraction setting and having sensors and an edge device
that are configured to monitor a manufacturing facility. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a natural resource
extraction setting and having sensors and an edge device that are
configured to monitor an underwater industrial setting. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a natural resource
extraction setting and having a sensor kit that collects sensor
data and a backend system that receives the sensor data from the
sensor kits and updates a distributed ledger based on the sensor
data. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a natural
resource extraction setting and having sensors and an edge device
that is configured to add new sensors to the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a natural resource
extraction setting and having sensors, an edge device, and a
gateway device that communicates with a communication network on
behalf of the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a natural resource extraction setting and having an edge
device that includes a data processing module that deduplicates,
filters, flags, and/or aggregates sensor data. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a natural resource extraction
setting and having an edge device that includes an encoding module
that encodes, compresses, and/or encrypts sensor data according to
one or more media codecs. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a natural resource extraction setting and having an edge
device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data. In embodiments, provided herein is a sensor
kit having sensors and an edge device that are configured to
monitor a natural resource extraction setting and having an edge
device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data. In embodiments, provided herein
is a sensor kit having sensors and an edge device that are
configured to monitor a natural resource extraction setting and
having an edge device that includes a configuration module that
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
natural resource extraction setting and having an edge device that
includes a distributed ledger module configured to update a
distributed ledger with sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a natural resource
extraction setting and having a backend system that includes a
decoding module that decrypts, decodes, and/or decompresses encoded
sensor kit packets. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
natural resource extraction setting and having a backend system
that includes a data processing module that executes a workflow
associated with a potential issue based on sensor data captured by
the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
natural resource extraction setting and having a backend system
that includes an AI module that trains machine-learned models to
make predictions or classifications related to sensor data captured
by a sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
natural resource extraction setting and having a backend system
that includes a notification module that issues notifications to
users when an issue is detected in an industrial setting based on
collected sensor data. In embodiments, provided herein is a sensor
kit having sensors and an edge device that are configured to
monitor a natural resource extraction setting and having a backend
system that includes an analytics module that performs analytics
tasks on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a natural resource extraction
setting and having a backend system that includes a control module
that provides commands to a device or system in an industrial
setting to take remedial action in response to a particular issue
being detected. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
natural resource extraction setting and having a backend system
that includes a dashboard module that presents a dashboard to a
human user that provides the human user with raw sensor data,
analytical data, and/or predictions or classifications based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor a natural resource extraction setting and
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor a natural resource
extraction setting and having a sensor kit and a backend system
that includes a configuration module that maintains configurations
of the sensor kit and configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a natural resource extraction
setting and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor a natural resource
extraction setting and having a sensor kit and a backend system
that updates a smart contract defining a condition that may trigger
an action based on sensor data received from the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a natural resource
extraction setting and having a distributed ledger that is at least
partially shared with a regulatory body to provide information
related to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a natural resource
extraction setting and having sensor kit and a backend system that
updates a smart contract, wherein the smart contract verifies one
or more conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a natural resource extraction
setting and having a sensor, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit.
[0349] In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a
pipeline setting. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
pipeline setting and having sensors and an edge device that are
configured to monitor a manufacturing facility. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a pipeline setting and having
sensors and an edge device that are configured to monitor an
underwater industrial setting. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a pipeline setting and having a sensor kit that collects
sensor data and a backend system that receives the sensor data from
the sensor kits and updates a distributed ledger based on the
sensor data. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a
pipeline setting and having sensors and an edge device that is
configured to add new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a pipeline setting and having
sensors, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a pipeline setting and
having an edge device that includes a data processing module that
deduplicates, filters, flags, and/or aggregates sensor data. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a pipeline setting and
having an edge device that includes an encoding module that
encodes, compresses, and/or encrypts sensor data according to one
or more media codecs. In embodiments, provided herein is a sensor
kit having sensors and an edge device that are configured to
monitor a pipeline setting and having an edge device that includes
a quick-decision AI module that uses machine-learned models to
generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor a pipeline setting
and having an edge device that includes a notification module that
provides notifications and/or alarms to users based on sensor data
and/or rules applied to the sensor data. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor a pipeline setting and having an edge device
that includes a configuration module that configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a pipeline setting and
having an edge device that includes a distributed ledger module
configured to update a distributed ledger with sensor data captured
by the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
pipeline setting and having a backend system that includes a
decoding module that decrypts, decodes, and/or decompresses encoded
sensor kit packets. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
pipeline setting and having a backend system that includes a data
processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a pipeline setting and
having a backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a pipeline setting and having a
backend system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor a pipeline setting and having a backend
system that includes an analytics module that performs analytics
tasks on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a pipeline setting and having a
backend system that includes a control module that provides
commands to a device or system in an industrial setting to take
remedial action in response to a particular issue being detected.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor a pipeline setting
and having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides the human user
with raw sensor data, analytical data, and/or predictions or
classifications based on sensor data received from the sensor kit.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor a pipeline setting
and having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor a pipeline setting
and having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a pipeline setting and having a sensor kit and a backend
system that updates a distributed ledger based on sensor data
provided by the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a pipeline setting and having a sensor kit and a backend
system that updates a smart contract defining a condition that may
trigger an action based on sensor data received from the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
and an edge device that are configured to monitor a pipeline
setting and having a distributed ledger that is at least partially
shared with a regulatory body to provide information related to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a pipeline setting and having sensor
kit and a backend system that updates a smart contract, wherein the
smart contract verifies one or more conditions put forth by a
regulatory body with respect to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
pipeline setting and having a sensor, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit.
[0350] In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a
manufacturing facility. In embodiments, provided herein is a sensor
kit having sensors and an edge device that are configured to
monitor a manufacturing facility and having sensors and an edge
device that are configured to monitor an underwater industrial
setting. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a
manufacturing facility and having a sensor kit that collects sensor
data and a backend system that receives the sensor data from the
sensor kits and updates a distributed ledger based on the sensor
data. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a
manufacturing facility and having sensors and an edge device that
is configured to add new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a manufacturing facility and having
sensors, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a manufacturing facility
and having an edge device that includes a data processing module
that deduplicates, filters, flags, and/or aggregates sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor a manufacturing
facility and having an edge device that includes an encoding module
that encodes, compresses, and/or encrypts sensor data according to
one or more media codecs. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a manufacturing facility and having an edge device that
includes a quick-decision AI module that uses machine-learned
models to generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor a manufacturing
facility and having an edge device that includes a notification
module that provides notifications and/or alarms to users based on
sensor data and/or rules applied to the sensor data. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a manufacturing facility
and having an edge device that includes a configuration module that
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor a
manufacturing facility and having an edge device that includes a
distributed ledger module configured to update a distributed ledger
with sensor data captured by the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a manufacturing facility and having
a backend system that includes a decoding module that decrypts,
decodes, and/or decompresses encoded sensor kit packets. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a manufacturing facility
and having a backend system that includes a data processing module
that executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor a manufacturing facility and having a backend
system that includes an AI module that trains machine-learned
models to make predictions or classifications related to sensor
data captured by a sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a manufacturing facility and having a backend system that
includes a notification module that issues notifications to users
when an issue is detected in an industrial setting based on
collected sensor data. In embodiments, provided herein is a sensor
kit having sensors and an edge device that are configured to
monitor a manufacturing facility and having a backend system that
includes an analytics module that performs analytics tasks on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor a manufacturing facility and having a backend
system that includes a control module that provides commands to a
device or system in an industrial setting to take remedial action
in response to a particular issue being detected. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a manufacturing facility and having
a backend system that includes a dashboard module that presents a
dashboard to a human user that provides the human user with raw
sensor data, analytical data, and/or predictions or classifications
based on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor a manufacturing facility and having
a backend system that includes a dashboard module that presents a
dashboard to a human user that provides a graphical user interface
that allows the user to configure the sensor kit system. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a manufacturing facility
and having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor a manufacturing facility and having a sensor kit and a
backend system that updates a distributed ledger based on sensor
data provided by the sensor kit. In embodiments, provided herein is
a sensor kit having sensors and an edge device that are configured
to monitor a manufacturing facility and having a sensor kit and a
backend system that updates a smart contract defining a condition
that may trigger an action based on sensor data received from the
sensor kit. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a
manufacturing facility and having a distributed ledger that is at
least partially shared with a regulatory body to provide
information related to compliance with a regulation or regulatory
action. In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor a
manufacturing facility and having sensor kit and a backend system
that updates a smart contract, wherein the smart contract verifies
one or more conditions put forth by a regulatory body with respect
to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor a manufacturing facility
and having a sensor, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit.
[0351] In embodiments, provided herein is a sensor kit having
sensors and an edge device that are configured to monitor an
underwater industrial setting. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor an underwater industrial setting and having a sensor kit
that collects sensor data and a backend system that receives the
sensor data from the sensor kits and updates a distributed ledger
based on the sensor data. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor an underwater industrial setting and having sensors and an
edge device that is configured to add new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
and an edge device that are configured to monitor an underwater
industrial setting and having sensors, an edge device, and a
gateway device that communicates with a communication network on
behalf of the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor an underwater industrial setting and having an edge device
that includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor an underwater industrial setting and having
an edge device that includes an encoding module that encodes,
compresses, and/or encrypts sensor data according to one or more
media codecs. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor an
underwater industrial setting and having an edge device that
includes a quick-decision AI module that uses machine-learned
models to generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor an underwater
industrial setting and having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that are configured to monitor an underwater
industrial setting and having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor an underwater industrial setting and
having an edge device that includes a distributed ledger module
configured to update a distributed ledger with sensor data captured
by the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor an
underwater industrial setting and having a backend system that
includes a decoding module that decrypts, decodes, and/or
decompresses encoded sensor kit packets. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor an underwater industrial setting and having a
backend system that includes a data processing module that executes
a workflow associated with a potential issue based on sensor data
captured by the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor an underwater industrial setting and having a backend
system that includes an AI module that trains machine-learned
models to make predictions or classifications related to sensor
data captured by a sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor an underwater industrial setting and having a backend
system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor an underwater industrial setting and having a
backend system that includes an analytics module that performs
analytics tasks on sensor data received from the sensor kit. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that are configured to monitor an underwater industrial
setting and having a backend system that includes a control module
that provides commands to a device or system in an industrial
setting to take remedial action in response to a particular issue
being detected. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor an
underwater industrial setting and having a backend system that
includes a dashboard module that presents a dashboard to a human
user that provides the human user with raw sensor data, analytical
data, and/or predictions or classifications based on sensor data
received from the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor an underwater industrial setting and having a backend
system that includes a dashboard module that presents a dashboard
to a human user that provides a graphical user interface that
allows the user to configure the sensor kit system. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that are configured to monitor an underwater industrial setting and
having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that are configured to
monitor an underwater industrial setting and having a sensor kit
and a backend system that updates a distributed ledger based on
sensor data provided by the sensor kit. In embodiments, provided
herein is a sensor kit having sensors and an edge device that are
configured to monitor an underwater industrial setting and having a
sensor kit and a backend system that updates a smart contract
defining a condition that may trigger an action based on sensor
data received from the sensor kit. In embodiments, provided herein
is a sensor kit having sensors and an edge device that are
configured to monitor an underwater industrial setting and having a
distributed ledger that is at least partially shared with a
regulatory body to provide information related to compliance with a
regulation or regulatory action. In embodiments, provided herein is
a sensor kit having sensors and an edge device that are configured
to monitor an underwater industrial setting and having sensor kit
and a backend system that updates a smart contract, wherein the
smart contract verifies one or more conditions put forth by a
regulatory body with respect to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
having sensors and an edge device that are configured to monitor an
underwater industrial setting and having a sensor, an edge device,
and a gateway device that communicates with a communication network
on behalf of the sensor kit.
[0352] In embodiments, provided herein is a sensor kit system
having a sensor kit that collects sensor data and a backend system
that receives the sensor data from the sensor kits and updates a
distributed ledger based on the sensor data. In embodiments,
provided herein is a sensor kit system having a sensor kit that
collects sensor data and a backend system that receives the sensor
data from the sensor kits and updates a distributed ledger based on
the sensor data and having sensors and an edge device that is
configured to add new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit system having a sensor kit that
collects sensor data and a backend system that receives the sensor
data from the sensor kits and updates a distributed ledger based on
the sensor data and having sensors, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit. In embodiments, provided herein is a sensor kit
system having a sensor kit that collects sensor data and a backend
system that receives the sensor data from the sensor kits and
updates a distributed ledger based on the sensor data and having an
edge device that includes a data processing module that
deduplicates, filters, flags, and/or aggregates sensor data. In
embodiments, provided herein is a sensor kit system having a sensor
kit that collects sensor data and a backend system that receives
the sensor data from the sensor kits and updates a distributed
ledger based on the sensor data and having an edge device that
includes an encoding module that encodes, compresses, and/or
encrypts sensor data according to one or more media codecs. In
embodiments, provided herein is a sensor kit system having a sensor
kit that collects sensor data and a backend system that receives
the sensor data from the sensor kits and updates a distributed
ledger based on the sensor data and having an edge device that
includes a quick-decision AI module that uses machine-learned
models to generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit system having a
sensor kit that collects sensor data and a backend system that
receives the sensor data from the sensor kits and updates a
distributed ledger based on the sensor data and having an edge
device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data. In embodiments, provided herein
is a sensor kit system having a sensor kit that collects sensor
data and a backend system that receives the sensor data from the
sensor kits and updates a distributed ledger based on the sensor
data and having an edge device that includes a configuration module
that configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein is a sensor kit
system having a sensor kit that collects sensor data and a backend
system that receives the sensor data from the sensor kits and
updates a distributed ledger based on the sensor data and having an
edge device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit. In embodiments, provided herein is a sensor kit system having
a sensor kit that collects sensor data and a backend system that
receives the sensor data from the sensor kits and updates a
distributed ledger based on the sensor data and having a backend
system that includes a decoding module that decrypts, decodes,
and/or decompresses encoded sensor kit packets. In embodiments,
provided herein is a sensor kit system having a sensor kit that
collects sensor data and a backend system that receives the sensor
data from the sensor kits and updates a distributed ledger based on
the sensor data and having a backend system that includes a data
processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit system having a sensor
kit that collects sensor data and a backend system that receives
the sensor data from the sensor kits and updates a distributed
ledger based on the sensor data and having a backend system that
includes an AI module that trains machine-learned models to make
predictions or classifications related to sensor data captured by a
sensor kit. In embodiments, provided herein is a sensor kit system
having a sensor kit that collects sensor data and a backend system
that receives the sensor data from the sensor kits and updates a
distributed ledger based on the sensor data and having a backend
system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. In embodiments, provided
herein is a sensor kit system having a sensor kit that collects
sensor data and a backend system that receives the sensor data from
the sensor kits and updates a distributed ledger based on the
sensor data and having a backend system that includes an analytics
module that performs analytics tasks on sensor data received from
the sensor kit. In embodiments, provided herein is a sensor kit
system having a sensor kit that collects sensor data and a backend
system that receives the sensor data from the sensor kits and
updates a distributed ledger based on the sensor data and having a
backend system that includes a control module that provides
commands to a device or system in an industrial setting to take
remedial action in response to a particular issue being detected.
In embodiments, provided herein is a sensor kit system having a
sensor kit that collects sensor data and a backend system that
receives the sensor data from the sensor kits and updates a
distributed ledger based on the sensor data and having a backend
system that includes a dashboard module that presents a dashboard
to a human user that provides the human user with raw sensor data,
analytical data, and/or predictions or classifications based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a sensor kit that collects
sensor data and a backend system that receives the sensor data from
the sensor kits and updates a distributed ledger based on the
sensor data and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides a
graphical user interface that allows the user to configure the
sensor kit system. In embodiments, provided herein is a sensor kit
system having a sensor kit that collects sensor data and a backend
system that receives the sensor data from the sensor kits and
updates a distributed ledger based on the sensor data and having a
sensor kit and a backend system that includes a configuration
module that maintains configurations of the sensor kit and
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein is a sensor kit
system having a sensor kit that collects sensor data and a backend
system that receives the sensor data from the sensor kits and
updates a distributed ledger based on the sensor data and having a
sensor kit and a backend system that updates a distributed ledger
based on sensor data provided by the sensor kit. In embodiments,
provided herein is a sensor kit system having a sensor kit that
collects sensor data and a backend system that receives the sensor
data from the sensor kits and updates a distributed ledger based on
the sensor data and having a sensor kit and a backend system that
updates a smart contract defining a condition that may trigger an
action based on sensor data received from the sensor kit. In
embodiments, provided herein is a sensor kit system having a sensor
kit that collects sensor data and a backend system that receives
the sensor data from the sensor kits and updates a distributed
ledger based on the sensor data and having a distributed ledger
that is at least partially shared with a regulatory body to provide
information related to compliance with a regulation or regulatory
action. In embodiments, provided herein is a sensor kit system
having a sensor kit that collects sensor data and a backend system
that receives the sensor data from the sensor kits and updates a
distributed ledger based on the sensor data and having sensor kit
and a backend system that updates a smart contract, wherein the
smart contract verifies one or more conditions put forth by a
regulatory body with respect to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
system having a sensor kit that collects sensor data and a backend
system that receives the sensor data from the sensor kits and
updates a distributed ledger based on the sensor data and having a
sensor, an edge device, and a gateway device that communicates with
a communication network on behalf of the sensor kit.
[0353] In embodiments, provided herein is a sensor kit having
sensors and an edge device that is configured to add new sensors to
the sensor kit. In embodiments, provided herein is a sensor kit
having sensors and an edge device that is configured to add new
sensors to the sensor kit and having sensors, an edge device, and a
gateway device that communicates with a communication network on
behalf of the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that is configured to
add new sensors to the sensor kit and having an edge device that
includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data. In embodiments, provided
herein is a sensor kit having sensors and an edge device that is
configured to add new sensors to the sensor kit and having an edge
device that includes an encoding module that encodes, compresses,
and/or encrypts sensor data according to one or more media codecs.
In embodiments, provided herein is a sensor kit having sensors and
an edge device that is configured to add new sensors to the sensor
kit and having an edge device that includes a quick-decision AI
module that uses machine-learned models to generate predictions
related to and/or classifications of industrial components based on
features of collected sensor data. In embodiments, provided herein
is a sensor kit having sensors and an edge device that is
configured to add new sensors to the sensor kit and having an edge
device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data. In embodiments, provided herein
is a sensor kit having sensors and an edge device that is
configured to add new sensors to the sensor kit and having an edge
device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having sensors
and an edge device that is configured to add new sensors to the
sensor kit and having an edge device that includes a distributed
ledger module configured to update a distributed ledger with sensor
data captured by the sensor kit. In embodiments, provided herein is
a sensor kit having sensors and an edge device that is configured
to add new sensors to the sensor kit and having a backend system
that includes a decoding module that decrypts, decodes, and/or
decompresses encoded sensor kit packets. In embodiments, provided
herein is a sensor kit having sensors and an edge device that is
configured to add new sensors to the sensor kit and having a
backend system that includes a data processing module that executes
a workflow associated with a potential issue based on sensor data
captured by the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that is configured to
add new sensors to the sensor kit and having a backend system that
includes an AI module that trains machine-learned models to make
predictions or classifications related to sensor data captured by a
sensor kit. In embodiments, provided herein is a sensor kit having
sensors and an edge device that is configured to add new sensors to
the sensor kit and having a backend system that includes a
notification module that issues notifications to users when an
issue is detected in an industrial setting based on collected
sensor data. In embodiments, provided herein is a sensor kit having
sensors and an edge device that is configured to add new sensors to
the sensor kit and having a backend system that includes an
analytics module that performs analytics tasks on sensor data
received from the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that is configured to
add new sensors to the sensor kit and having a backend system that
includes a control module that provides commands to a device or
system in an industrial setting to take remedial action in response
to a particular issue being detected. In embodiments, provided
herein is a sensor kit having sensors and an edge device that is
configured to add new sensors to the sensor kit and having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides the human user with raw
sensor data, analytical data, and/or predictions or classifications
based on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that is configured to add new sensors to the sensor kit and having
a backend system that includes a dashboard module that presents a
dashboard to a human user that provides a graphical user interface
that allows the user to configure the sensor kit system. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that is configured to add new sensors to the sensor kit
and having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit having sensors and an edge device that is configured to
add new sensors to the sensor kit and having a sensor kit and a
backend system that updates a distributed ledger based on sensor
data provided by the sensor kit. In embodiments, provided herein is
a sensor kit having sensors and an edge device that is configured
to add new sensors to the sensor kit and having a sensor kit and a
backend system that updates a smart contract defining a condition
that may trigger an action based on sensor data received from the
sensor kit. In embodiments, provided herein is a sensor kit having
sensors and an edge device that is configured to add new sensors to
the sensor kit and having a distributed ledger that is at least
partially shared with a regulatory body to provide information
related to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit having sensors and an
edge device that is configured to add new sensors to the sensor kit
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit having sensors and an edge device
that is configured to add new sensors to the sensor kit and having
a sensor, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit.
[0354] In embodiments, provided herein is a sensor kit having
sensors, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit. In
embodiments, provided herein is a sensor kit having sensors, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit and having an
edge device that includes a data processing module that
deduplicates, filters, flags, and/or aggregates sensor data. In
embodiments, provided herein is a sensor kit having sensors, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit and having an
edge device that includes an encoding module that encodes,
compresses, and/or encrypts sensor data according to one or more
media codecs. In embodiments, provided herein is a sensor kit
having sensors, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit and having an edge device that includes a quick-decision AI
module that uses machine-learned models to generate predictions
related to and/or classifications of industrial components based on
features of collected sensor data. In embodiments, provided herein
is a sensor kit having sensors, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit and having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data.
In embodiments, provided herein is a sensor kit having sensors, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit and having an
edge device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having
sensors, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit and having
an edge device that includes a distributed ledger module configured
to update a distributed ledger with sensor data captured by the
sensor kit. In embodiments, provided herein is a sensor kit having
sensors, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit and having
a backend system that includes a decoding module that decrypts,
decodes, and/or decompresses encoded sensor kit packets. In
embodiments, provided herein is a sensor kit having sensors, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit and having a
backend system that includes a data processing module that executes
a workflow associated with a potential issue based on sensor data
captured by the sensor kit. In embodiments, provided herein is a
sensor kit having sensors, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit and having a backend system that includes an AI module
that trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit. In
embodiments, provided herein is a sensor kit having sensors, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit and having a
backend system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. In embodiments, provided
herein is a sensor kit having sensors, an edge device, and a
gateway device that communicates with a communication network on
behalf of the sensor kit and having a backend system that includes
an analytics module that performs analytics tasks on sensor data
received from the sensor kit. In embodiments, provided herein is a
sensor kit having sensors, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit and having a backend system that includes a control
module that provides commands to a device or system in an
industrial setting to take remedial action in response to a
particular issue being detected. In embodiments, provided herein is
a sensor kit having sensors, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides the
human user with raw sensor data, analytical data, and/or
predictions or classifications based on sensor data received from
the sensor kit. In embodiments, provided herein is a sensor kit
having sensors, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit and having a backend system that includes a dashboard module
that presents a dashboard to a human user that provides a graphical
user interface that allows the user to configure the sensor kit
system. In embodiments, provided herein is a sensor kit having
sensors, an edge device, and a gateway device that communicates
with a communication network on behalf of the sensor kit and having
a sensor kit and a backend system that includes a configuration
module that maintains configurations of the sensor kit and
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein is a sensor kit
having sensors, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein is a sensor kit having sensors, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit and having a
sensor kit and a backend system that updates a smart contract
defining a condition that may trigger an action based on sensor
data received from the sensor kit. In embodiments, provided herein
is a sensor kit having sensors, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit and having a distributed ledger that is at least
partially shared with a regulatory body to provide information
related to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit having sensors, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit and having sensor
kit and a backend system that updates a smart contract, wherein the
smart contract verifies one or more conditions put forth by a
regulatory body with respect to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
having sensors, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit and having a sensor, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit.
[0355] In embodiments, provided herein is a sensor kit having an
edge device that includes a data processing module that
deduplicates, filters, flags, and/or aggregates sensor data. In
embodiments, provided herein is a sensor kit having an edge device
that includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data and having an edge device that
includes an encoding module that encodes, compresses, and/or
encrypts sensor data according to one or more media codecs. In
embodiments, provided herein is a sensor kit having an edge device
that includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data and having an edge device that
includes a quick-decision AI module that uses machine-learned
models to generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit having an edge
device that includes a data processing module that deduplicates,
filters, flags, and/or aggregates sensor data and having an edge
device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data. In embodiments, provided herein
is a sensor kit having an edge device that includes a data
processing module that deduplicates, filters, flags, and/or
aggregates sensor data and having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a data processing module that deduplicates, filters, flags, and/or
aggregates sensor data and having an edge device that includes a
distributed ledger module configured to update a distributed ledger
with sensor data captured by the sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a data processing module that deduplicates, filters, flags, and/or
aggregates sensor data and having a backend system that includes a
decoding module that decrypts, decodes, and/or decompresses encoded
sensor kit packets. In embodiments, provided herein is a sensor kit
having an edge device that includes a data processing module that
deduplicates, filters, flags, and/or aggregates sensor data and
having a backend system that includes a data processing module that
executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit. In embodiments, provided
herein is a sensor kit having an edge device that includes a data
processing module that deduplicates, filters, flags, and/or
aggregates sensor data and having a backend system that includes an
AI module that trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit. In
embodiments, provided herein is a sensor kit having an edge device
that includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data and having a backend system
that includes a notification module that issues notifications to
users when an issue is detected in an industrial setting based on
collected sensor data. In embodiments, provided herein is a sensor
kit having an edge device that includes a data processing module
that deduplicates, filters, flags, and/or aggregates sensor data
and having a backend system that includes an analytics module that
performs analytics tasks on sensor data received from the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes a data processing module that deduplicates,
filters, flags, and/or aggregates sensor data and having a backend
system that includes a control module that provides commands to a
device or system in an industrial setting to take remedial action
in response to a particular issue being detected. In embodiments,
provided herein is a sensor kit having an edge device that includes
a data processing module that deduplicates, filters, flags, and/or
aggregates sensor data and having a backend system that includes a
dashboard module that presents a dashboard to a human user that
provides the human user with raw sensor data, analytical data,
and/or predictions or classifications based on sensor data received
from the sensor kit. In embodiments, provided herein is a sensor
kit having an edge device that includes a data processing module
that deduplicates, filters, flags, and/or aggregates sensor data
and having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system.
In embodiments, provided herein is a sensor kit having an edge
device that includes a data processing module that deduplicates,
filters, flags, and/or aggregates sensor data and having a sensor
kit and a backend system that includes a configuration module that
maintains configurations of the sensor kit and configures a sensor
kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes a data processing module that deduplicates,
filters, flags, and/or aggregates sensor data and having a sensor
kit and a backend system that updates a distributed ledger based on
sensor data provided by the sensor kit. In embodiments, provided
herein is a sensor kit having an edge device that includes a data
processing module that deduplicates, filters, flags, and/or
aggregates sensor data and having a sensor kit and a backend system
that updates a smart contract defining a condition that may trigger
an action based on sensor data received from the sensor kit. In
embodiments, provided herein is a sensor kit having an edge device
that includes a data processing module that deduplicates, filters,
flags, and/or aggregates sensor data and having a distributed
ledger that is at least partially shared with a regulatory body to
provide information related to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
having an edge device that includes a data processing module that
deduplicates, filters, flags, and/or aggregates sensor data and
having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit having an edge device that includes
a data processing module that deduplicates, filters, flags, and/or
aggregates sensor data and having a sensor, an edge device, and a
gateway device that communicates with a communication network on
behalf of the sensor kit.
[0356] In embodiments, provided herein is a sensor kit having an
edge device that includes an encoding module that encodes,
compresses, and/or encrypts sensor data according to one or more
media codecs. In embodiments, provided herein is a sensor kit
having an edge device that includes an encoding module that
encodes, compresses, and/or encrypts sensor data according to one
or more media codecs and having an edge device that includes a
quick-decision AI module that uses machine-learned models to
generate predictions related to and/or classifications of
industrial components based on features of collected sensor data.
In embodiments, provided herein is a sensor kit having an edge
device that includes an encoding module that encodes, compresses,
and/or encrypts sensor data according to one or more media codecs
and having an edge device that includes a notification module that
provides notifications and/or alarms to users based on sensor data
and/or rules applied to the sensor data. In embodiments, provided
herein is a sensor kit having an edge device that includes an
encoding module that encodes, compresses, and/or encrypts sensor
data according to one or more media codecs and having an edge
device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes an encoding module that encodes, compresses,
and/or encrypts sensor data according to one or more media codecs
and having an edge device that includes a distributed ledger module
configured to update a distributed ledger with sensor data captured
by the sensor kit. In embodiments, provided herein is a sensor kit
having an edge device that includes an encoding module that
encodes, compresses, and/or encrypts sensor data according to one
or more media codecs and having a backend system that includes a
decoding module that decrypts, decodes, and/or decompresses encoded
sensor kit packets. In embodiments, provided herein is a sensor kit
having an edge device that includes an encoding module that
encodes, compresses, and/or encrypts sensor data according to one
or more media codecs and having a backend system that includes a
data processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit having an edge device
that includes an encoding module that encodes, compresses, and/or
encrypts sensor data according to one or more media codecs and
having a backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
an encoding module that encodes, compresses, and/or encrypts sensor
data according to one or more media codecs and having a backend
system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. In embodiments, provided
herein is a sensor kit having an edge device that includes an
encoding module that encodes, compresses, and/or encrypts sensor
data according to one or more media codecs and having a backend
system that includes an analytics module that performs analytics
tasks on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
an encoding module that encodes, compresses, and/or encrypts sensor
data according to one or more media codecs and having a backend
system that includes a control module that provides commands to a
device or system in an industrial setting to take remedial action
in response to a particular issue being detected. In embodiments,
provided herein is a sensor kit having an edge device that includes
an encoding module that encodes, compresses, and/or encrypts sensor
data according to one or more media codecs and having a backend
system that includes a dashboard module that presents a dashboard
to a human user that provides the human user with raw sensor data,
analytical data, and/or predictions or classifications based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having an edge device that includes an
encoding module that encodes, compresses, and/or encrypts sensor
data according to one or more media codecs and having a backend
system that includes a dashboard module that presents a dashboard
to a human user that provides a graphical user interface that
allows the user to configure the sensor kit system. In embodiments,
provided herein is a sensor kit having an edge device that includes
an encoding module that encodes, compresses, and/or encrypts sensor
data according to one or more media codecs and having a sensor kit
and a backend system that includes a configuration module that
maintains configurations of the sensor kit and configures a sensor
kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes an encoding module that encodes, compresses,
and/or encrypts sensor data according to one or more media codecs
and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein is a sensor kit having an edge
device that includes an encoding module that encodes,
compresses,
[0357] Attorney Docket No. 15015-000041/US/COD and/or encrypts
sensor data according to one or more media codecs and having a
sensor kit and a backend system that updates a smart contract
defining a condition that may trigger an action based on sensor
data received from the sensor kit. In embodiments, provided herein
is a sensor kit having an edge device that includes an encoding
module that encodes, compresses, and/or encrypts sensor data
according to one or more media codecs and having a distributed
ledger that is at least partially shared with a regulatory body to
provide information related to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
having an edge device that includes an encoding module that
encodes, compresses, and/or encrypts sensor data according to one
or more media codecs and having sensor kit and a backend system
that updates a smart contract, wherein the smart contract verifies
one or more conditions put forth by a regulatory body with respect
to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit having an edge device
that includes an encoding module that encodes, compresses, and/or
encrypts sensor data according to one or more media codecs and
having a sensor, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit.
[0358] In embodiments, provided herein is a sensor kit having an
edge device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data. In embodiments, provided herein is a sensor
kit having an edge device that includes a quick-decision AI module
that uses machine-learned models to generate predictions related to
and/or classifications of industrial components based on features
of collected sensor data and having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data.
In embodiments, provided herein is a sensor kit having an edge
device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data and having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a quick-decision AI module that uses machine-learned models to
generate predictions related to and/or classifications of
industrial components based on features of collected sensor data
and having an edge device that includes a distributed ledger module
configured to update a distributed ledger with sensor data captured
by the sensor kit. In embodiments, provided herein is a sensor kit
having an edge device that includes a quick-decision AI module that
uses machine-learned models to generate predictions related to
and/or classifications of industrial components based on features
of collected sensor data and having a backend system that includes
a decoding module that decrypts, decodes, and/or decompresses
encoded sensor kit packets. In embodiments, provided herein is a
sensor kit having an edge device that includes a quick-decision AI
module that uses machine-learned models to generate predictions
related to and/or classifications of industrial components based on
features of collected sensor data and having a backend system that
includes a data processing module that executes a workflow
associated with a potential issue based on sensor data captured by
the sensor kit. In embodiments, provided herein is a sensor kit
having an edge device that includes a quick-decision AI module that
uses machine-learned models to generate predictions related to
and/or classifications of industrial components based on features
of collected sensor data and having a backend system that includes
an AI module that trains machine-learned models to make predictions
or classifications related to sensor data captured by a sensor kit.
In embodiments, provided herein is a sensor kit having an edge
device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data and having a backend system that includes a
notification module that issues notifications to users when an
issue is detected in an industrial setting based on collected
sensor data. In embodiments, provided herein is a sensor kit having
an edge device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data and having a backend system that includes an
analytics module that performs analytics tasks on sensor data
received from the sensor kit. In embodiments, provided herein is a
sensor kit having an edge device that includes a quick-decision AI
module that uses machine-learned models to generate predictions
related to and/or classifications of industrial components based on
features of collected sensor data and having a backend system that
includes a control module that provides commands to a device or
system in an industrial setting to take remedial action in response
to a particular issue being detected. In embodiments, provided
herein is a sensor kit having an edge device that includes a
quick-decision AI module that uses machine-learned models to
generate predictions related to and/or classifications of
industrial components based on features of collected sensor data
and having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides the human user
with raw sensor data, analytical data, and/or predictions or
classifications based on sensor data received from the sensor kit.
In embodiments, provided herein is a sensor kit having an edge
device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data and having a backend system that includes a
dashboard module that presents a dashboard to a human user that
provides a graphical user interface that allows the user to
configure the sensor kit system. In embodiments, provided herein is
a sensor kit having an edge device that includes a quick-decision
AI module that uses machine-learned models to generate predictions
related to and/or classifications of industrial components based on
features of collected sensor data and having a sensor kit and a
backend system that includes a configuration module that maintains
configurations of the sensor kit and configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit. In
embodiments, provided herein is a sensor kit having an edge device
that includes a quick-decision AI module that uses machine-learned
models to generate predictions related to and/or classifications of
industrial components based on features of collected sensor data
and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein is a sensor kit having an edge
device that includes a quick-decision AI module that uses
machine-learned models to generate predictions related to and/or
classifications of industrial components based on features of
collected sensor data and having a sensor kit and a backend system
that updates a smart contract defining a condition that may trigger
an action based on sensor data received from the sensor kit. In
embodiments, provided herein is a sensor kit having an edge device
that includes a quick-decision AI module that uses machine-learned
models to generate predictions related to and/or classifications of
industrial components based on features of collected sensor data
and having a distributed ledger that is at least partially shared
with a regulatory body to provide information related to compliance
with a regulation or regulatory action. In embodiments, provided
herein is a sensor kit having an edge device that includes a
quick-decision AI module that uses machine-learned models to
generate predictions related to and/or classifications of
industrial components based on features of collected sensor data
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit having an edge device that includes
a quick-decision AI module that uses machine-learned models to
generate predictions related to and/or classifications of
industrial components based on features of collected sensor data
and having a sensor, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit.
[0359] In embodiments, provided herein is a sensor kit having an
edge device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data. In embodiments, provided herein
is a sensor kit having an edge device that includes a notification
module that provides notifications and/or alarms to users based on
sensor data and/or rules applied to the sensor data and having an
edge device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data and having an edge device that
includes a distributed ledger module configured to update a
distributed ledger with sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit having an edge device
that includes a notification module that provides notifications
and/or alarms to users based on sensor data and/or rules applied to
the sensor data and having a backend system that includes a
decoding module that decrypts, decodes, and/or decompresses encoded
sensor kit packets. In embodiments, provided herein is a sensor kit
having an edge device that includes a notification module that
provides notifications and/or alarms to users based on sensor data
and/or rules applied to the sensor data and having a backend system
that includes a data processing module that executes a workflow
associated with a potential issue based on sensor data captured by
the sensor kit. In embodiments, provided herein is a sensor kit
having an edge device that includes a notification module that
provides notifications and/or alarms to users based on sensor data
and/or rules applied to the sensor data and having a backend system
that includes an AI module that trains machine-learned models to
make predictions or classifications related to sensor data captured
by a sensor kit. In embodiments, provided herein is a sensor kit
having an edge device that includes a notification module that
provides notifications and/or alarms to users based on sensor data
and/or rules applied to the sensor data and having a backend system
that includes a notification module that issues notifications to
users when an issue is detected in an industrial setting based on
collected sensor data. In embodiments, provided herein is a sensor
kit having an edge device that includes a notification module that
provides notifications and/or alarms to users based on sensor data
and/or rules applied to the sensor data and having a backend system
that includes an analytics module that performs analytics tasks on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data
and having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected. In embodiments, provided herein is a sensor kit having an
edge device that includes a notification module that provides
notifications and/or alarms to users based on sensor data and/or
rules applied to the sensor data and having a backend system that
includes a dashboard module that presents a dashboard to a human
user that provides the human user with raw sensor data, analytical
data, and/or predictions or classifications based on sensor data
received from the sensor kit. In embodiments, provided herein is a
sensor kit having an edge device that includes a notification
module that provides notifications and/or alarms to users based on
sensor data and/or rules applied to the sensor data and having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides a graphical user interface
that allows the user to configure the sensor kit system. In
embodiments, provided herein is a sensor kit having an edge device
that includes a notification module that provides notifications
and/or alarms to users based on sensor data and/or rules applied to
the sensor data and having a sensor kit and a backend system that
includes a configuration module that maintains configurations of
the sensor kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit having an edge device that includes a notification
module that provides notifications and/or alarms to users based on
sensor data and/or rules applied to the sensor data and having a
sensor kit and a backend system that updates a distributed ledger
based on sensor data provided by the sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data
and having a sensor kit and a backend system that updates a smart
contract defining a condition that may trigger an action based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data
and having a distributed ledger that is at least partially shared
with a regulatory body to provide information related to compliance
with a regulation or regulatory action. In embodiments, provided
herein is a sensor kit having an edge device that includes a
notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit having an edge device that includes
a notification module that provides notifications and/or alarms to
users based on sensor data and/or rules applied to the sensor data
and having a sensor, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit.
[0360] In embodiments, provided herein is a sensor kit having an
edge device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor kit
and having an edge device that includes a distributed ledger module
configured to update a distributed ledger with sensor data captured
by the sensor kit. In embodiments, provided herein is a sensor kit
having an edge device that includes a configuration module that
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit and having a backend system that includes a
decoding module that decrypts, decodes, and/or decompresses encoded
sensor kit packets. In embodiments, provided herein is a sensor kit
having an edge device that includes a configuration module that
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit and having a backend system that includes a data
processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit. In
embodiments, provided herein is a sensor kit having an edge device
that includes a configuration module that configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit and having a
backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit and having a backend
system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. In embodiments, provided
herein is a sensor kit having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit and having a backend
system that includes an analytics module that performs analytics
tasks on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit and having a backend
system that includes a control module that provides commands to a
device or system in an industrial setting to take remedial action
in response to a particular issue being detected. In embodiments,
provided herein is a sensor kit having an edge device that includes
a configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit and having a backend
system that includes a dashboard module that presents a dashboard
to a human user that provides the human user with raw sensor data,
analytical data, and/or predictions or classifications based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit and having a backend
system that includes a dashboard module that presents a dashboard
to a human user that provides a graphical user interface that
allows the user to configure the sensor kit system. In embodiments,
provided herein is a sensor kit having an edge device that includes
a configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit and having a sensor kit
and a backend system that includes a configuration module that
maintains configurations of the sensor kit and configures a sensor
kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor kit
and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein is a sensor kit having an edge
device that includes a configuration module that configures a
sensor kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor kit
and having a sensor kit and a backend system that updates a smart
contract defining a condition that may trigger an action based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit having an edge device that includes a
configuration module that configures a sensor kit network by
transmitting configuration requests to sensor devices, generating
device records based on responses to the configuration requests,
and/or adding new sensors to the sensor kit and having a
distributed ledger that is at least partially shared with a
regulatory body to provide information related to compliance with a
regulation or regulatory action. In embodiments, provided herein is
a sensor kit having an edge device that includes a configuration
module that configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit and having sensor kit and a backend
system that updates a smart contract, wherein the smart contract
verifies one or more conditions put forth by a regulatory body with
respect to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit having an edge device
that includes a configuration module that configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit and having a
sensor, an edge device, and a gateway device that communicates with
a communication network on behalf of the sensor kit.
[0361] In embodiments, provided herein is a sensor kit having an
edge device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit and having a backend system that includes a decoding module
that decrypts, decodes, and/or decompresses encoded sensor kit
packets. In embodiments, provided herein is a sensor kit having an
edge device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit and having a backend system that includes a data processing
module that executes a workflow associated with a potential issue
based on sensor data captured by the sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a distributed ledger module configured to update a distributed
ledger with sensor data captured by the sensor kit and having a
backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a distributed ledger module configured to update a distributed
ledger with sensor data captured by the sensor kit and having a
backend system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. In embodiments, provided
herein is a sensor kit having an edge device that includes a
distributed ledger module configured to update a distributed ledger
with sensor data captured by the sensor kit and having a backend
system that includes an analytics module that performs analytics
tasks on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit having an edge device that includes
a distributed ledger module configured to update a distributed
ledger with sensor data captured by the sensor kit and having a
backend system that includes a control module that provides
commands to a device or system in an industrial setting to take
remedial action in response to a particular issue being detected.
In embodiments, provided herein is a sensor kit having an edge
device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit and having a backend system that includes a dashboard module
that presents a dashboard to a human user that provides the human
user with raw sensor data, analytical data, and/or predictions or
classifications based on sensor data received from the sensor kit.
In embodiments, provided herein is a sensor kit having an edge
device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit and having a backend system that includes a dashboard module
that presents a dashboard to a human user that provides a graphical
user interface that allows the user to configure the sensor kit
system. In embodiments, provided herein is a sensor kit having an
edge device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit and having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit having an edge device that includes a distributed ledger
module configured to update a distributed ledger with sensor data
captured by the sensor kit and having a sensor kit and a backend
system that updates a distributed ledger based on sensor data
provided by the sensor kit. In embodiments, provided herein is a
sensor kit having an edge device that includes a distributed ledger
module configured to update a distributed ledger with sensor data
captured by the sensor kit and having a sensor kit and a backend
system that updates a smart contract defining a condition that may
trigger an action based on sensor data received from the sensor
kit. In embodiments, provided herein is a sensor kit having an edge
device that includes a distributed ledger module configured to
update a distributed ledger with sensor data captured by the sensor
kit and having a distributed ledger that is at least partially
shared with a regulatory body to provide information related to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit having an edge device that includes
a distributed ledger module configured to update a distributed
ledger with sensor data captured by the sensor kit and having
sensor kit and a backend system that updates a smart contract,
wherein the smart contract verifies one or more conditions put
forth by a regulatory body with respect to compliance with a
regulation or regulatory action. In embodiments, provided herein is
a sensor kit having an edge device that includes a distributed
ledger module configured to update a distributed ledger with sensor
data captured by the sensor kit and having a sensor, an edge
device, and a gateway device that communicates with a communication
network on behalf of the sensor kit.
[0362] In embodiments, provided herein is a sensor kit system
having a backend system that includes a decoding module that
decrypts, decodes, and/or decompresses encoded sensor kit packets.
In embodiments, provided herein is a sensor kit system having a
backend system that includes a decoding module that decrypts,
decodes, and/or decompresses encoded sensor kit packets and having
a backend system that includes a data processing module that
executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit. In embodiments, provided
herein is a sensor kit system having a backend system that includes
a decoding module that decrypts, decodes, and/or decompresses
encoded sensor kit packets and having a backend system that
includes an AI module that trains machine-learned models to make
predictions or classifications related to sensor data captured by a
sensor kit. In embodiments, provided herein is a sensor kit system
having a backend system that includes a decoding module that
decrypts, decodes, and/or decompresses encoded sensor kit packets
and having a backend system that includes a notification module
that issues notifications to users when an issue is detected in an
industrial setting based on collected sensor data. In embodiments,
provided herein is a sensor kit system having a backend system that
includes a decoding module that decrypts, decodes, and/or
decompresses encoded sensor kit packets and having a backend system
that includes an analytics module that performs analytics tasks on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a backend system that includes
a decoding module that decrypts, decodes, and/or decompresses
encoded sensor kit packets and having a backend system that
includes a control module that provides commands to a device or
system in an industrial setting to take remedial action in response
to a particular issue being detected. In embodiments, provided
herein is a sensor kit system having a backend system that includes
a decoding module that decrypts, decodes, and/or decompresses
encoded sensor kit packets and having a backend system that
includes a dashboard module that presents a dashboard to a human
user that provides the human user with raw sensor data, analytical
data, and/or predictions or classifications based on sensor data
received from the sensor kit. In embodiments, provided herein is a
sensor kit system having a backend system that includes a decoding
module that decrypts, decodes, and/or decompresses encoded sensor
kit packets and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides a
graphical user interface that allows the user to configure the
sensor kit system. In embodiments, provided herein is a sensor kit
system having a backend system that includes a decoding module that
decrypts, decodes, and/or decompresses encoded sensor kit packets
and having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit system having a backend system that includes a decoding
module that decrypts, decodes, and/or decompresses encoded sensor
kit packets and having a sensor kit and a backend system that
updates a distributed ledger based on sensor data provided by the
sensor kit. In embodiments, provided herein is a sensor kit system
having a backend system that includes a decoding module that
decrypts, decodes, and/or decompresses encoded sensor kit packets
and having a sensor kit and a backend system that updates a smart
contract defining a condition that may trigger an action based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a backend system that includes
a decoding module that decrypts, decodes, and/or decompresses
encoded sensor kit packets and having a distributed ledger that is
at least partially shared with a regulatory body to provide
information related to compliance with a regulation or regulatory
action. In embodiments, provided herein is a sensor kit system
having a backend system that includes a decoding module that
decrypts, decodes, and/or decompresses encoded sensor kit packets
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a backend system that
includes a decoding module that decrypts, decodes, and/or
decompresses encoded sensor kit packets and having a sensor, an
edge device, and a gateway device that communicates with a
communication network on behalf of the sensor kit.
[0363] In embodiments, provided herein is a sensor kit system
having a backend system that includes a data processing module that
executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit. In embodiments, provided
herein is a sensor kit system having a backend system that includes
a data processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit and
having a backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit system having a backend system that
includes a data processing module that executes a workflow
associated with a potential issue based on sensor data captured by
the sensor kit and having a backend system that includes a
notification module that issues notifications to users when an
issue is detected in an industrial setting based on collected
sensor data. In embodiments, provided herein is a sensor kit system
having a backend system that includes a data processing module that
executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit and having a backend system
that includes an analytics module that performs analytics tasks on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a backend system that includes
a data processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit and
having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected. In embodiments, provided herein is a sensor kit system
having a backend system that includes a data processing module that
executes a workflow associated with a potential issue based on
sensor data captured by the sensor kit and having a backend system
that includes a dashboard module that presents a dashboard to a
human user that provides the human user with raw sensor data,
analytical data, and/or predictions or classifications based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a backend system that includes
a data processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit and
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system.
In embodiments, provided herein is a sensor kit system having a
backend system that includes a data processing module that executes
a workflow associated with a potential issue based on sensor data
captured by the sensor kit and having a sensor kit and a backend
system that includes a configuration module that maintains
configurations of the sensor kit and configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit. In
embodiments, provided herein is a sensor kit system having a
backend system that includes a data processing module that executes
a workflow associated with a potential issue based on sensor data
captured by the sensor kit and having a sensor kit and a backend
system that updates a distributed ledger based on sensor data
provided by the sensor kit. In embodiments, provided herein is a
sensor kit system having a backend system that includes a data
processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit and
having a sensor kit and a backend system that updates a smart
contract defining a condition that may trigger an action based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a backend system that includes
a data processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit and
having a distributed ledger that is at least partially shared with
a regulatory body to provide information related to compliance with
a regulation or regulatory action. In embodiments, provided herein
is a sensor kit system having a backend system that includes a data
processing module that executes a workflow associated with a
potential issue based on sensor data captured by the sensor kit and
having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a backend system that
includes a data processing module that executes a workflow
associated with a potential issue based on sensor data captured by
the sensor kit and having a sensor, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit.
[0364] In embodiments, provided herein is a sensor kit system
having a backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit. In embodiments,
provided herein is a sensor kit system having a backend system that
includes an AI module that trains machine-learned models to make
predictions or classifications related to sensor data captured by a
sensor kit and having a backend system that includes a notification
module that issues notifications to users when an issue is detected
in an industrial setting based on collected sensor data. In
embodiments, provided herein is a sensor kit system having a
backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit and having a
backend system that includes an analytics module that performs
analytics tasks on sensor data received from the sensor kit. In
embodiments, provided herein is a sensor kit system having a
backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit and having a
backend system that includes a control module that provides
commands to a device or system in an industrial setting to take
remedial action in response to a particular issue being detected.
In embodiments, provided herein is a sensor kit system having a
backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit and having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides the human user with raw
sensor data, analytical data, and/or predictions or classifications
based on sensor data received from the sensor kit. In embodiments,
provided herein is a sensor kit system having a backend system that
includes an AI module that trains machine-learned models to make
predictions or classifications related to sensor data captured by a
sensor kit and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides a
graphical user interface that allows the user to configure the
sensor kit system. In embodiments, provided herein is a sensor kit
system having a backend system that includes an AI module that
trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit and
having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit system having a backend system that includes an AI
module that trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit and
having a sensor kit and a backend system that updates a distributed
ledger based on sensor data provided by the sensor kit. In
embodiments, provided herein is a sensor kit system having a
backend system that includes an AI module that trains
machine-learned models to make predictions or classifications
related to sensor data captured by a sensor kit and having a sensor
kit and a backend system that updates a smart contract defining a
condition that may trigger an action based on sensor data received
from the sensor kit. In embodiments, provided herein is a sensor
kit system having a backend system that includes an AI module that
trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit and
having a distributed ledger that is at least partially shared with
a regulatory body to provide information related to compliance with
a regulation or regulatory action. In embodiments, provided herein
is a sensor kit system having a backend system that includes an AI
module that trains machine-learned models to make predictions or
classifications related to sensor data captured by a sensor kit and
having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a backend system that
includes an AI module that trains machine-learned models to make
predictions or classifications related to sensor data captured by a
sensor kit and having a sensor, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit.
[0365] In embodiments, provided herein is a sensor kit system
having a backend system that includes a notification module that
issues notifications to users when an issue is detected in an
industrial setting based on collected sensor data. In embodiments,
provided herein is a sensor kit system having a backend system that
includes a notification module that issues notifications to users
when an issue is detected in an industrial setting based on
collected sensor data and having a backend system that includes an
analytics module that performs analytics tasks on sensor data
received from the sensor kit. In embodiments, provided herein is a
sensor kit system having a backend system that includes a
notification module that issues notifications to users when an
issue is detected in an industrial setting based on collected
sensor data. and having a backend system that includes a control
module that provides commands to a device or system in an
industrial setting to take remedial action in response to a
particular issue being detected. In embodiments, provided herein is
a sensor kit system having a backend system that includes a
notification module that issues notifications to users when an
issue is detected in an industrial setting based on collected
sensor data. and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides the
human user with raw sensor data, analytical data, and/or
predictions or classifications based on sensor data received from
the sensor kit. In embodiments, provided herein is a sensor kit
system having a backend system that includes a notification module
that issues notifications to users when an issue is detected in an
industrial setting based on collected sensor data. and having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides a graphical user interface
that allows the user to configure the sensor kit system. In
embodiments, provided herein is a sensor kit system having a
backend system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. and having a sensor kit and
a backend system that includes a configuration module that
maintains configurations of the sensor kit and configures a sensor
kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor
kit. In embodiments, provided herein is a sensor kit system having
a backend system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. and having a sensor kit and
a backend system that updates a distributed ledger based on sensor
data provided by the sensor kit. In embodiments, provided herein is
a sensor kit system having a backend system that includes a
notification module that issues notifications to users when an
issue is detected in an industrial setting based on collected
sensor data. and having a sensor kit and a backend system that
updates a smart contract defining a condition that may trigger an
action based on sensor data received from the sensor kit. In
embodiments, provided herein is a sensor kit system having a
backend system that includes a notification module that issues
notifications to users when an issue is detected in an industrial
setting based on collected sensor data. and having a distributed
ledger that is at least partially shared with a regulatory body to
provide information related to compliance with a regulation or
regulatory action. In embodiments, provided herein is a sensor kit
system having a backend system that includes a notification module
that issues notifications to users when an issue is detected in an
industrial setting based on collected sensor data. and having
sensor kit and a backend system that updates a smart contract,
wherein the smart contract verifies one or more conditions put
forth by a regulatory body with respect to compliance with a
regulation or regulatory action. In embodiments, provided herein is
a sensor kit system having a backend system that includes a
notification module that issues notifications to users when an
issue is detected in an industrial setting based on collected
sensor data. and having a sensor, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit.
[0366] In embodiments, provided herein is a sensor kit system
having a backend system that includes an analytics module that
performs analytics tasks on sensor data received from the sensor
kit. In embodiments, provided herein is a sensor kit system having
a backend system that includes an analytics module that performs
analytics tasks on sensor data received from the sensor kit and
having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected. In embodiments, provided herein is a sensor kit system
having a backend system that includes an analytics module that
performs analytics tasks on sensor data received from the sensor
kit and having a backend system that includes a dashboard module
that presents a dashboard to a human user that provides the human
user with raw sensor data, analytical data, and/or predictions or
classifications based on sensor data received from the sensor kit.
In embodiments, provided herein is a sensor kit system having a
backend system that includes an analytics module that performs
analytics tasks on sensor data received from the sensor kit and
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system.
In embodiments, provided herein is a sensor kit system having a
backend system that includes an analytics module that performs
analytics tasks on sensor data received from the sensor kit and
having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit system having a backend system that includes an
analytics module that performs analytics tasks on sensor data
received from the sensor kit and having a sensor kit and a backend
system that updates a distributed ledger based on sensor data
provided by the sensor kit. In embodiments, provided herein is a
sensor kit system having a backend system that includes an
analytics module that performs analytics tasks on sensor data
received from the sensor kit and having a sensor kit and a backend
system that updates a smart contract defining a condition that may
trigger an action based on sensor data received from the sensor
kit. In embodiments, provided herein is a sensor kit system having
a backend system that includes an analytics module that performs
analytics tasks on sensor data received from the sensor kit and
having a distributed ledger that is at least partially shared with
a regulatory body to provide information related to compliance with
a regulation or regulatory action. In embodiments, provided herein
is a sensor kit system having a backend system that includes an
analytics module that performs analytics tasks on sensor data
received from the sensor kit and having sensor kit and a backend
system that updates a smart contract, wherein the smart contract
verifies one or more conditions put forth by a regulatory body with
respect to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit system having a
backend system that includes an analytics module that performs
analytics tasks on sensor data received from the sensor kit and
having a sensor, an edge device, and a gateway device that
communicates with a communication network on behalf of the sensor
kit.
[0367] In embodiments, provided herein is a sensor kit system
having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected. In embodiments, provided herein is a sensor kit system
having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides the
human user with raw sensor data, analytical data, and/or
predictions or classifications based on sensor data received from
the sensor kit. In embodiments, provided herein is a sensor kit
system having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected and having a backend system that includes a dashboard
module that presents a dashboard to a human user that provides a
graphical user interface that allows the user to configure the
sensor kit system. In embodiments, provided herein is a sensor kit
system having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected and having a sensor kit and a backend system that includes
a configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit system having a backend system that includes a control
module that provides commands to a device or system in an
industrial setting to take remedial action in response to a
particular issue being detected and having a sensor kit and a
backend system that updates a distributed ledger based on sensor
data provided by the sensor kit. In embodiments, provided herein is
a sensor kit system having a backend system that includes a control
module that provides commands to a device or system in an
industrial setting to take remedial action in response to a
particular issue being detected and having a sensor kit and a
backend system that updates a smart contract defining a condition
that may trigger an action based on sensor data received from the
sensor kit. In embodiments, provided herein is a sensor kit system
having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected and having a distributed ledger that is at least partially
shared with a regulatory body to provide information related to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a backend system that
includes a control module that provides commands to a device or
system in an industrial setting to take remedial action in response
to a particular issue being detected and having sensor kit and a
backend system that updates a smart contract, wherein the smart
contract verifies one or more conditions put forth by a regulatory
body with respect to compliance with a regulation or regulatory
action. In embodiments, provided herein is a sensor kit system
having a backend system that includes a control module that
provides commands to a device or system in an industrial setting to
take remedial action in response to a particular issue being
detected and having a sensor, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit.
[0368] In embodiments, provided herein is a sensor kit system
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides the human user
with raw sensor data, analytical data, and/or predictions or
classifications based on sensor data received from the sensor kit.
In embodiments, provided herein is a sensor kit system having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides the human user with raw
sensor data, analytical data, and/or predictions or classifications
based on sensor data received from the sensor kit and having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides a graphical user interface
that allows the user to configure the sensor kit system. In
embodiments, provided herein is a sensor kit system having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides the human user with raw
sensor data, analytical data, and/or predictions or classifications
based on sensor data received from the sensor kit and having a
sensor kit and a backend system that includes a configuration
module that maintains configurations of the sensor kit and
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein is a sensor kit
system having a backend system that includes a dashboard module
that presents a dashboard to a human user that provides the human
user with raw sensor data, analytical data, and/or predictions or
classifications based on sensor data received from the sensor kit
and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein is a sensor kit system having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides the human user with raw
sensor data, analytical data, and/or predictions or classifications
based on sensor data received from the sensor kit and having a
sensor kit and a backend system that updates a smart contract
defining a condition that may trigger an action based on sensor
data received from the sensor kit. In embodiments, provided herein
is a sensor kit system having a backend system that includes a
dashboard module that presents a dashboard to a human user that
provides the human user with raw sensor data, analytical data,
and/or predictions or classifications based on sensor data received
from the sensor kit and having a distributed ledger that is at
least partially shared with a regulatory body to provide
information related to compliance with a regulation or regulatory
action. In embodiments, provided herein is a sensor kit system
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides the human user
with raw sensor data, analytical data, and/or predictions or
classifications based on sensor data received from the sensor kit
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a backend system that
includes a dashboard module that presents a dashboard to a human
user that provides the human user with raw sensor data, analytical
data, and/or predictions or classifications based on sensor data
received from the sensor kit and having a sensor, an edge device,
and a gateway device that communicates with a communication network
on behalf of the sensor kit.
[0369] In embodiments, provided herein is a sensor kit system
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system.
In embodiments, provided herein is a sensor kit system having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides a graphical user interface
that allows the user to configure the sensor kit system and having
a sensor kit and a backend system that includes a configuration
module that maintains configurations of the sensor kit and
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit. In embodiments, provided herein is a sensor kit
system having a backend system that includes a dashboard module
that presents a dashboard to a human user that provides a graphical
user interface that allows the user to configure the sensor kit
system and having a sensor kit and a backend system that updates a
distributed ledger based on sensor data provided by the sensor kit.
In embodiments, provided herein is a sensor kit system having a
backend system that includes a dashboard module that presents a
dashboard to a human user that provides a graphical user interface
that allows the user to configure the sensor kit system and having
a sensor kit and a backend system that updates a smart contract
defining a condition that may trigger an action based on sensor
data received from the sensor kit. In embodiments, provided herein
is a sensor kit system having a backend system that includes a
dashboard module that presents a dashboard to a human user that
provides a graphical user interface that allows the user to
configure the sensor kit system and having a distributed ledger
that is at least partially shared with a regulatory body to provide
information related to compliance with a regulation or regulatory
action. In embodiments, provided herein is a sensor kit system
having a backend system that includes a dashboard module that
presents a dashboard to a human user that provides a graphical user
interface that allows the user to configure the sensor kit system
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a backend system that
includes a dashboard module that presents a dashboard to a human
user that provides a graphical user interface that allows the user
to configure the sensor kit system and having a sensor, an edge
device, and a gateway device that communicates with a communication
network on behalf of the sensor kit.
[0370] In embodiments, provided herein is a sensor kit system
having a sensor kit and a backend system that includes a
configuration module that maintains configurations of the sensor
kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit. In embodiments, provided herein is a
sensor kit system having a sensor kit and a backend system that
includes a configuration module that maintains configurations of
the sensor kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit and having a sensor kit and a backend
system that updates a distributed ledger based on sensor data
provided by the sensor kit. In embodiments, provided herein is a
sensor kit system having a sensor kit and a backend system that
includes a configuration module that maintains configurations of
the sensor kit and configures a sensor kit network by transmitting
configuration requests to sensor devices, generating device records
based on responses to the configuration requests, and/or adding new
sensors to the sensor kit and having a sensor kit and a backend
system that updates a smart contract defining a condition that may
trigger an action based on sensor data received from the sensor
kit. In embodiments, provided herein is a sensor kit system having
a sensor kit and a backend system that includes a configuration
module that maintains configurations of the sensor kit and
configures a sensor kit network by transmitting configuration
requests to sensor devices, generating device records based on
responses to the configuration requests, and/or adding new sensors
to the sensor kit and having a distributed ledger that is at least
partially shared with a regulatory body to provide information
related to compliance with a regulation or regulatory action. In
embodiments, provided herein is a sensor kit system having a sensor
kit and a backend system that includes a configuration module that
maintains configurations of the sensor kit and configures a sensor
kit network by transmitting configuration requests to sensor
devices, generating device records based on responses to the
configuration requests, and/or adding new sensors to the sensor kit
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a sensor kit and a
backend system that includes a configuration module that maintains
configurations of the sensor kit and configures a sensor kit
network by transmitting configuration requests to sensor devices,
generating device records based on responses to the configuration
requests, and/or adding new sensors to the sensor kit and having a
sensor, an edge device, and a gateway device that communicates with
a communication network on behalf of the sensor kit.
[0371] In embodiments, provided herein is a sensor kit system
having a sensor kit and a backend system that updates a distributed
ledger based on sensor data provided by the sensor kit. In
embodiments, provided herein is a sensor kit system having a sensor
kit and a backend system that updates a distributed ledger based on
sensor data provided by the sensor kit and having a sensor kit and
a backend system that updates a smart contract defining a condition
that may trigger an action based on sensor data received from the
sensor kit. In embodiments, provided herein is a sensor kit system
having a sensor kit and a backend system that updates a distributed
ledger based on sensor data provided by the sensor kit and having a
distributed ledger that is at least partially shared with a
regulatory body to provide information related to compliance with a
regulation or regulatory action. In embodiments, provided herein is
a sensor kit system having a sensor kit and a backend system that
updates a distributed ledger based on sensor data provided by the
sensor kit and having sensor kit and a backend system that updates
a smart contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a sensor kit and a
backend system that updates a distributed ledger based on sensor
data provided by the sensor kit and having a sensor, an edge
device, and a gateway device that communicates with a communication
network on behalf of the sensor kit.
[0372] In embodiments, provided herein is a sensor kit system
having a sensor kit and a backend system that updates a smart
contract defining a condition that may trigger an action based on
sensor data received from the sensor kit. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system that updates a smart contract defining a condition that may
trigger an action based on sensor data received from the sensor kit
and having a distributed ledger that is at least partially shared
with a regulatory body to provide information related to compliance
with a regulation or regulatory action. In embodiments, provided
herein is a sensor kit system having a sensor kit and a backend
system that updates a smart contract defining a condition that may
trigger an action based on sensor data received from the sensor kit
and having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a sensor kit and a
backend system that updates a smart contract defining a condition
that may trigger an action based on sensor data received from the
sensor kit and having a sensor, an edge device, and a gateway
device that communicates with a communication network on behalf of
the sensor kit.
[0373] In embodiments, provided herein is a sensor kit system
having a distributed ledger that is at least partially shared with
a regulatory body to provide information related to compliance with
a regulation or regulatory action. In embodiments, provided herein
is a sensor kit system having a distributed ledger that is at least
partially shared with a regulatory body to provide information
related to compliance with a regulation or regulatory action and
having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having a distributed ledger
that is at least partially shared with a regulatory body to provide
information related to compliance with a regulation or regulatory
action and having a sensor, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit.
[0374] In embodiments, provided herein is a sensor kit system
having sensor kit and a backend system that updates a smart
contract, wherein the smart contract verifies one or more
conditions put forth by a regulatory body with respect to
compliance with a regulation or regulatory action. In embodiments,
provided herein is a sensor kit system having sensor kit and a
backend system that updates a smart contract, wherein the smart
contract verifies one or more conditions put forth by a regulatory
body with respect to compliance with a regulation or regulatory
action and having a sensor, an edge device, and a gateway device
that communicates with a communication network on behalf of the
sensor kit.
[0375] In embodiments, provided herein is a sensor kit having a
sensor, an edge device, and a gateway device that communicates with
a communication network on behalf of the sensor kit.
[0376] Detailed embodiments of the present disclosure are disclosed
herein; however, it is to be understood that the disclosed
embodiments are merely exemplary of the disclosure, which may be
embodied in various forms. Therefore, specific structural and
functional details disclosed herein are not to be interpreted as
limiting, but merely as a basis for the claims and as a
representative basis for teaching one skilled in the art to
variously employ the present disclosure in virtually any
appropriately detailed structure.
[0377] The terms "a" or "an," as used herein, are defined as one or
more than one. The term "another," as used herein, is defined as at
least a second or more. The terms "including" and/or "having," as
used herein, are defined as comprising (i.e., open transition).
[0378] While only a few embodiments of the present disclosure have
been shown and described, it will be obvious to those skilled in
the art that many changes and modifications may be made thereunto
without departing from the spirit and scope of the present
disclosure as described in the following claims. All patent
applications and patents, both foreign and domestic, and all other
publications referenced herein are incorporated herein in their
entireties to the full extent permitted by law.
[0379] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software,
program codes, and/or instructions on a processor. The present
disclosure may be implemented as a method on the machine, as a
system or apparatus as part of or in relation to the machine, or as
a computer program product embodied in a computer readable medium
executing on one or more of the machines. In embodiments, the
processor may be part of a server, cloud server, client, network
infrastructure, mobile computing platform, stationary computing
platform, or other computing platforms. A processor may be any kind
of computational or processing device capable of executing program
instructions, codes, binary instructions and the like. The
processor may be or may include a signal processor, digital
processor, embedded processor, microprocessor or any variant such
as a co-processor (math co-processor, graphic co-processor,
communication co-processor and the like) and the like that may
directly or indirectly facilitate execution of program code or
program instructions stored thereon. In addition, the processor may
enable the execution of multiple programs, threads, and codes. The
threads may be executed simultaneously to enhance the performance
of the processor and to facilitate simultaneous operations of the
application. By way of implementation, methods, program codes,
program instructions and the like described herein may be
implemented in one or more threads. The thread may spawn other
threads that may have assigned priorities associated with them; the
processor may execute these threads based on priority or any other
order based on instructions provided in the program code. The
processor, or any machine utilizing one, may include non-transitory
memory that stores methods, codes, instructions and programs as
described herein and elsewhere. The processor may access a
non-transitory storage medium through an interface that may store
methods, codes, and instructions as described herein and elsewhere.
The storage medium associated with the processor for storing
methods, programs, codes, program instructions or other type of
instructions capable of being executed by the computing or
processing device may include but may not be limited to one or more
of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache
and the like.
[0380] A processor may include one or more cores that may enhance
speed and performance of a multiprocessor. In embodiments, the
process may be a dual core processor, quad core processors, other
chip-level multiprocessor and the like that combine two or more
independent cores (called a die).
[0381] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software
on a server, client, firewall, gateway, hub, router, or other such
computer and/or networking hardware. The software program may be
associated with a server that may include a file server, print
server, domain server, Internet server, intranet server, cloud
server, and other variants such as a secondary server, host server,
distributed server and the like. The server may include one or more
of memories, processors, computer readable media, storage media,
ports (physical and virtual), communication devices, and interfaces
capable of accessing other servers, clients, machines, and devices
through a wired or a wireless medium, and the like. The methods,
programs, or codes as described herein and elsewhere may be
executed by the server. In addition, other devices required for
execution of methods as described in this application may be
considered as a part of the infrastructure associated with the
server.
[0382] The server may provide an interface to other devices
including, without limitation, clients, other servers, printers,
database servers, print servers, file servers, communication
servers, distributed servers, social networks, and the like.
Additionally, this coupling and/or connection may facilitate remote
execution of programs across the network. The networking of some or
all of these devices may facilitate parallel processing of a
program or method at one or more locations without deviating from
the scope of the disclosure. In addition, any of the devices
attached to the server through an interface may include at least
one storage medium capable of storing methods, programs, code
and/or instructions. A central repository may provide program
instructions to be executed on different devices. In this
implementation, the remote repository may act as a storage medium
for program code, instructions, and programs.
[0383] The software program may be associated with a client that
may include a file client, print client, domain client, Internet
client, intranet client and other variants such as secondary
client, host client, distributed client and the like. The client
may include one or more of memories, processors, computer readable
media, storage media, ports (physical and virtual), communication
devices, and interfaces capable of accessing other clients,
servers, machines, and devices through a wired or a wireless
medium, and the like. The methods, programs, or codes as described
herein and elsewhere may be executed by the client. In addition,
other devices required for the execution of methods as described in
this application may be considered as a part of the infrastructure
associated with the client.
[0384] The client may provide an interface to other devices
including, without limitation, servers, other clients, printers,
database servers, print servers, file servers, communication
servers, distributed servers and the like. Additionally, this
coupling and/or connection may facilitate remote execution of
programs across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more locations without deviating from the scope of the
disclosure. In addition, any of the devices attached to the client
through an interface may include at least one storage medium
capable of storing methods, programs, applications, code and/or
instructions. A central repository may provide program instructions
to be executed on different devices. In this implementation, the
remote repository may act as a storage medium for program code,
instructions, and programs.
[0385] The methods and systems described herein may be deployed in
part or in whole through network infrastructures. The network
infrastructure may include elements such as computing devices,
servers, routers, hubs, firewalls, clients, personal computers,
communication devices, routing devices and other active and passive
devices, modules and/or components as known in the art. The
computing and/or non-computing device(s) associated with the
network infrastructure may include, apart from other components, a
storage medium such as flash memory, buffer, stack, RAM, ROM and
the like. The processes, methods, program codes, instructions
described herein and elsewhere may be executed by one or more of
the network infrastructural elements. The methods and systems
described herein may be adapted for use with any kind of private,
community, or hybrid cloud computing network or cloud computing
environment, including those which involve features of software as
a service (SaaS), platform as a service (PaaS), and/or
infrastructure as a service (IaaS).
[0386] The methods, program codes, and instructions described
herein and elsewhere may be implemented on a cellular network
having multiple cells. The cellular network may either be frequency
division multiple access (FDMA) network or code division multiple
access (CDMA) network. The cellular network may include mobile
devices, cell sites, base stations, repeaters, antennas, towers,
and the like. The cell network may be a GSM, GPRS, 3G, EVDO, mesh,
or other network types.
[0387] The methods, program codes, and instructions described
herein and elsewhere may be implemented on or through mobile
devices. The mobile devices may include navigation devices, cell
phones, mobile phones, mobile personal digital assistants, laptops,
palmtops, netbooks, pagers, electronic book readers, music players
and the like. These devices may include, apart from other
components, a storage medium such as a flash memory, buffer, RAM,
ROM and one or more computing devices. The computing devices
associated with mobile devices may be enabled to execute program
codes, methods, and instructions stored thereon. Alternatively, the
mobile devices may be configured to execute instructions in
collaboration with other devices. The mobile devices may
communicate with base stations interfaced with servers and
configured to execute program codes. The mobile devices may
communicate on a peer-to-peer network, mesh network, or other
communications network. The program code may be stored on the
storage medium associated with the server and executed by a
computing device embedded within the server. The base station may
include a computing device and a storage medium. The storage device
may store program codes and instructions executed by the computing
devices associated with the base station.
[0388] The computer software, program codes, and/or instructions
may be stored and/or accessed on machine readable media that may
include: computer components, devices, and recording media that
retain digital data used for computing for some interval of time;
semiconductor storage known as random access memory (RAM); mass
storage typically for more permanent storage, such as optical
discs, forms of magnetic storage like hard disks, tapes, drums,
cards and other types; processor registers, cache memory, volatile
memory, non-volatile memory; optical storage such as CD, DVD;
removable media such as flash memory (e.g., USB sticks or keys),
floppy disks, magnetic tape, paper tape, punch cards, standalone
RAM disks, Zip drives, removable mass storage, off-line, and the
like; other computer memory such as dynamic memory, static memory,
read/write storage, mutable storage, read only, random access,
sequential access, location addressable, file addressable, content
addressable, network attached storage, storage area network, bar
codes, magnetic ink, and the like.
[0389] The methods and systems described herein may transform
physical and/or intangible items from one state to another. The
methods and systems described herein may also transform data
representing physical and/or intangible items from one state to
another.
[0390] The elements described and depicted herein, including in
flowcharts and block diagrams throughout the figures, imply logical
boundaries between the elements. However, according to software or
hardware engineering practices, the depicted elements and the
functions thereof may be implemented on machines through computer
executable media having a processor capable of executing program
instructions stored thereon as a monolithic software structure, as
standalone software modules, or as modules that employ external
routines, code, services, and so forth, or any combination of
these, and all such implementations may be within the scope of the
present disclosure. Examples of such machines may include, but may
not be limited to, personal digital assistants, laptops, personal
computers, mobile phones, other handheld computing devices, medical
equipment, wired or wireless communication devices, transducers,
chips, calculators, satellites, tablet PCs, electronic books,
gadgets, electronic devices, devices having artificial
intelligence, computing devices, networking equipment, servers,
routers and the like. Furthermore, the elements depicted in the
flowchart and block diagrams or any other logical component may be
implemented on a machine capable of executing program instructions.
Thus, while the foregoing drawings and descriptions set forth
functional aspects of the disclosed systems, no particular
arrangement of software for implementing these functional aspects
should be inferred from these descriptions unless explicitly stated
or otherwise clear from the context. Similarly, it will be
appreciated that the various steps identified and described above
may be varied and that the order of steps may be adapted to
particular applications of the techniques disclosed herein. All
such variations and modifications are intended to fall within the
scope of this disclosure. As such, the depiction and/or description
of an order for various steps should not be understood to require a
particular order of execution for those steps, unless required by a
particular application, or explicitly stated or otherwise clear
from the context.
[0391] The methods and/or processes described above, and steps
associated therewith, may be realized in hardware, software or any
combination of hardware and software suitable for a particular
application. The hardware may include a general-purpose computer
and/or dedicated computing device or specific computing device or
particular aspect or component of a specific computing device. The
processes may be realized in one or more microprocessors,
microcontrollers, embedded microcontrollers, programmable digital
signal processors or other programmable devices, along with
internal and/or external memory. The processes may also, or
instead, be embodied in an application specific integrated circuit,
a programmable gate array, programmable array logic, or any other
device or combination of devices that may be configured to process
electronic signals. It will further be appreciated that one or more
of the processes may be realized as a computer executable code
capable of being executed on a machine-readable medium. The
computer executable code may be created using a structured
programming language such as C, an object oriented programming
language such as C++, or any other high-level or low-level
programming language (including assembly languages, hardware
description languages, and database programming languages and
technologies) that may be stored, compiled or interpreted to run on
one of the above devices, as well as heterogeneous combinations of
processors, processor architectures, or combinations of different
hardware and software, or any other machine capable of executing
program instructions.
[0392] Thus, in one aspect, methods described above and
combinations thereof may be embodied in computer executable code
that, when executing on one or more computing devices, performs the
steps thereof. In another aspect, the methods may be embodied in
systems that perform the steps thereof, and may be distributed
across devices in a number of ways, or all of the functionality may
be integrated into a dedicated, standalone device or other
hardware. In another aspect, the means for performing the steps
associated with the processes described above may include any of
the hardware and/or software described above. All such permutations
and combinations are intended to fall within the scope of the
present disclosure.
[0393] While the disclosure has been disclosed in connection with
the preferred embodiments shown and described in detail, various
modifications and improvements thereon will become readily apparent
to those skilled in the art. Accordingly, the spirit and scope of
the present disclosure is not to be limited by the foregoing
examples but is to be understood in the broadest sense allowable by
law.
[0394] The use of the terms "a" and "an" and "the" and similar
referents in the context of describing the disclosure (especially
in the context of the following claims) is to be construed to cover
both the singular and the plural unless otherwise indicated herein
or clearly contradicted by context. The terms "comprising,"
"having," "including," and "containing" are to be construed as
open-ended terms (i.e., meaning "including, but not limited to,")
unless otherwise noted. Recitations of ranges of values herein are
merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range,
unless otherwise indicated herein, and each separate value is
incorporated into the specification as if it were individually
recited herein. All methods described herein may be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (e.g., "such as") provided herein, is
intended merely to better illuminate the disclosure and does not
pose a limitation on the scope of the disclosure unless otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element as essential to the practice of
the disclosure.
[0395] While the foregoing written description enables one skilled
in the art to make and use what is considered presently to be the
best mode thereof, those skilled in the art will understand and
appreciate the existence of variations, combinations, and
equivalents of the specific embodiment, method, and examples
herein. The disclosure should therefore not be limited by the
above-described embodiment, method, and examples, but by all
embodiments and methods within the scope and spirit of the
disclosure.
[0396] Any element in a claim that does not explicitly state "means
for" performing a specified function, or "step for" performing a
specified function, is not to be interpreted as a "means" or "step"
clause as specified in 35 U.S.C. .sctn. 112(f). In particular, any
use of "step of" in the claims is not intended to invoke the
provision of 35 U. S. C. .sctn. 112(f).
[0397] Persons skilled in the art may appreciate that numerous
design configurations may be possible to enjoy the functional
benefits of the inventive systems. Thus, given the wide variety of
configurations and arrangements of embodiments of the present
invention the scope of the invention is reflected by the breadth of
the claims below rather than narrowed by the embodiments described
above.
* * * * *