U.S. patent application number 16/657709 was filed with the patent office on 2020-02-13 for collaborative geo-positioning of electronic devices.
The applicant listed for this patent is Satori Worldwide, LLC. Invention is credited to Caleb Tolman.
Application Number | 20200053514 16/657709 |
Document ID | / |
Family ID | 63519775 |
Filed Date | 2020-02-13 |
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United States Patent
Application |
20200053514 |
Kind Code |
A1 |
Tolman; Caleb |
February 13, 2020 |
COLLABORATIVE GEO-POSITIONING OF ELECTRONIC DEVICES
Abstract
Methods and systems for determining and tracking electronic
devices are provided. One method includes obtaining measurement
data from each of at least a subset of a plurality of client
devices and determining, by a computer processing device,
geolocation estimation data for one or more of the subset of the
plurality of client devices based at least in part on the
measurement data. The method further determining, by the computer
processing device, a geolocation confidence value based at least in
part on the geolocation estimation data, wherein the geolocation
confidence value indicates a level of confidence of the geolocation
estimation data. The method further includes providing the
geolocation estimation data and the geolocation confidence value to
the one or more of the subset of the plurality of client
devices.
Inventors: |
Tolman; Caleb; (Palo Alto,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Satori Worldwide, LLC |
Palo Alto |
CA |
US |
|
|
Family ID: |
63519775 |
Appl. No.: |
16/657709 |
Filed: |
October 18, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15925219 |
Mar 19, 2018 |
10499193 |
|
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16657709 |
|
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62473726 |
Mar 20, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01S 5/02 20130101; H04L
67/26 20130101; G01S 5/0205 20130101; H04W 4/026 20130101; H04W
64/00 20130101; H04W 4/12 20130101; H04W 4/029 20180201 |
International
Class: |
H04W 4/029 20060101
H04W004/029; G01S 5/02 20060101 G01S005/02; H04W 4/02 20060101
H04W004/02; H04W 4/12 20060101 H04W004/12; H04L 29/08 20060101
H04L029/08; H04W 64/00 20060101 H04W064/00 |
Claims
1. A method, comprising: obtaining measurement data from each of at
least a subset of a plurality of client devices; determining, by a
computer processing device, geolocation estimation data for one or
more of the subset of the plurality of client devices based at
least in part on the measurement data; determining, by the computer
processing device, a geolocation confidence value based at least in
part on the geolocation estimation data, wherein the geolocation
confidence value indicates a level of confidence of the geolocation
estimation data; and providing the geolocation estimation data and
the geolocation confidence value to the one or more of the subset
of the plurality of client devices.
2. The method of claim 1, wherein the measurement data is generated
by each respective client device of the subset of the plurality of
client devices.
3. The method of claim 1, wherein the measurement data comprises at
least one of distance data, radio frequency signal intensity data,
or latency data.
4. The method of claim 1, wherein the geolocation estimation data
comprises a longitude coordinate, a latitude coordinate, and an
altitude coordinate, and wherein the longitude coordinate, the
latitude coordinate, and the altitude coordinate indicate an
approximate location of at least another one of the subset of the
plurality of client devices.
5. The method of claim 1, wherein the geolocation estimation data
comprises a radial estimation, and wherein the radial estimation
indicates a radius in which at least another one of the subset of
the plurality of client devices resides.
6. The method of claim 1, wherein the geolocation estimation data
comprises an indication of whether a client device of the plurality
of client devices desiring to be located is stationary or in
motion.
7. The method of claim 1, comprising: obtaining a desired
operational parameter from the one or more of the subset of the
plurality of client devices; and determining measurement
instructions based at least in part on the desired operational
parameter.
8. The method of claim 7, comprising: transmitting the measurement
instructions to the one or more of the subset of the plurality of
client devices.
9. The method of claim 1, comprising: determining an approximate
physical location of the one or more of the subset of the plurality
of client devices based on the geolocation estimation data and the
geolocation confidence value.
10. The method of claim 1, wherein the geolocation estimation data
and the geolocation confidence value are determined with respect to
the one or more of the subset of the plurality of client devices
based at least in part on one or more geolocation estimation
models.
11. An apparatus, comprising: a computer processing device, the
computer processing device to: obtain measurement data from each of
at least a subset of a plurality of client devices; determine
geolocation estimation data for one or more of the subset of the
plurality of client devices based at least in part on the
measurement data; determine a geolocation confidence value based at
least in part on the geolocation estimation data, wherein the
geolocation confidence value indicates a level of confidence of the
geolocation estimation data; and provide the geolocation estimation
data and the geolocation confidence value to the one or more of the
subset of the plurality of client devices.
12. The apparatus of claim 11, wherein the measurement data is
generated by each respective client device of the subset of the
plurality of client devices.
13. The apparatus of claim 11, wherein the measurement data
comprises at least one of distance data, radio frequency signal
intensity data, or latency data.
14. The apparatus of claim 11, wherein the geolocation estimation
data comprises a longitude coordinate, a latitude coordinate, and
an altitude coordinate, and wherein the longitude coordinate, the
latitude coordinate, and the altitude coordinate indicate an
approximate location of at least another one of the subset of the
plurality of client devices.
15. The apparatus of claim 11, wherein the geolocation estimation
data comprises a radial estimation, and wherein the radial
estimation indicates a radius in which at least another one of the
subset of the plurality of client devices resides.
16. The apparatus of claim 11, wherein the geolocation estimation
data comprises an indication of whether a client device of the
plurality of client devices desiring to be located is stationary or
in motion.
17. The apparatus of claim 11, wherein the computer processing
device is further to: obtain a desired operational parameter from
the one or more of the subset of the plurality of client devices;
and determine measurement instructions based at least in part on
the desired operational parameter.
18. The apparatus of claim 17, wherein the computer processing
device is further to: transmit the measurement instructions to the
one or more of the subset of the plurality of client devices.
19. The apparatus of claim 11, wherein the computer processing
device is further to: determine an approximate physical location of
the one or more of the subset of the plurality of client devices
based on the geolocation estimation data and the geolocation
confidence value.
20. A non-transitory computer-readable medium having instruction
stored thereon that, when executed by a computer processing device,
cause the computer processing device to: obtain measurement data
from each of at least a subset of a plurality of client devices;
determine, by the computer processing device, geolocation
estimation data for one or more of the subset of the plurality of
client devices based at least in part on the measurement data;
determine a geolocation confidence value based at least in part on
the geolocation estimation data, wherein the geolocation confidence
value indicates a level of confidence of the geolocation estimation
data; and provide the geolocation estimation data and the
geolocation confidence value to the one or more of the subset of
the plurality of client devices.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 15/925,219, filed Mar. 19, 2018, which claims the benefit of
U.S. Provisional Patent Application No. 62/473,726, filed Mar. 20,
2017, the entire contents of each of which are hereby incorporated
by reference.
BACKGROUND
[0002] This specification relates to a data communication system
and, in particular, systems and methods for determining and
tracking the approximate geolocation of electronic devices.
[0003] The publish-subscribe (or "PubSub") pattern is a data
communication messaging arrangement implemented by software systems
where so-called publishers publish messages to topics and so-called
subscribers receive the messages pertaining to particular topics to
which they are subscribed. There can be one or more publishers per
topic and publishers generally have no knowledge of what
subscribers, if any, will receive the published messages. Because
publishers may publish large volumes of messages, and subscribers
may subscribe to many topics (or "channels") the overall volume of
messages directed to a particular channel and/or subscriber may be
difficult to manage.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1A illustrates an example system that supports the
PubSub communication pattern.
[0005] FIG. 1B illustrates functional layers of software on an
example client device.
[0006] FIG. 2 is a diagram of an example messaging system.
[0007] FIG. 3A is a data flow diagram of an example method for
writing data to a streamlet.
[0008] FIG. 3B is a data flow diagram of an example method for
reading data from a streamlet.
[0009] FIG. 4A is a data flow diagram of an example method for
publishing messages to a channel of a messaging system.
[0010] FIG. 4B is a data flow diagram of an example method for
subscribing to a channel of a messaging system.
[0011] FIG. 4C is an example data structure for storing messages of
a channel of a messaging system.
[0012] FIG. 5A is a data flow diagram of an example method for
publishing and replicating messages of a messaging system.
[0013] FIG. 5B is a data flow diagram of an example method for
retrieving stored messages in a messaging system.
[0014] FIGS. 5C and 5D are data flow diagrams of example methods
for repairing a chain of copies of data in a messaging system.
[0015] FIG. 6 is an example data flow diagram for the application
of filtering criteria in a messaging system.
[0016] FIGS. 7A-7D are illustrations of how messages may be
processed using query instructions that include a period-based
parameter.
[0017] FIG. 8 is a diagram illustrating an example map of an
environment in which one or more electronic devices may be
determined and tracked.
[0018] FIG. 9 is a diagram of an example system architecture that
may be used to track and locate electronic devices in an
environment.
[0019] FIG. 10 is a flowchart of an example method for determining
and tracking the approximate geolocation of electronic devices in
an environment.
[0020] FIG. 11 is a flowchart of an example method for calculating
geolocation estimation data and for calculating a geolocation
confidence value.
[0021] FIG. 12 is a flowchart of an example method for determining
and tracking the approximate geolocation of electronic devices in
an environment.
[0022] FIG. 13 is a block diagram of an example computing device
that may perform one or more of the operations described herein, in
accordance with the present embodiments.
DETAILED DESCRIPTION
[0023] Elements of examples or embodiments described with respect
to a given aspect of the invention can be used in various
embodiments of another aspect of the invention. For example, it is
contemplated that features of dependent claims depending from one
independent claim can be used in apparatus, systems, and/or methods
of any of the other independent claims.
[0024] The details of one or more embodiments of the subject matter
described in this specification are set forth in the accompanying
drawings and the description below. Other features, aspects, and
advantages of the subject matter will become apparent from the
description, the drawings, and the claims.
[0025] A system architecture for locating and tracking electronic
devices in indoor and outdoor environments may include a messaging
system. The messaging system may support the PubSub communication
pattern and may allow publishers and subscribers to publish and
receive live messages. Users of certain electronic devices may want
to determine and track other electronic devices, and thus may
include both publishers and subscribers of the messaging system.
Electronic devices may publish messages that indicate approximate
geolocations of the electronic devices. Users may view the messages
and corresponding approximate geolocations of the electronic
devices as the users of the electronic devices move within or about
indoor or outdoor environments.
[0026] For example, the present embodiments may be directed to a
schema for collaborative geo-positioning of multiple electronic
devices that may improve triangulation accuracy and precision of
locating one or more electronic devices of a number of electronic
devices by calculating and recording information that may be used
to improve accuracy and precision of future geolocation estimates.
The present techniques also minimize the amount of information
stored on servers that could be used to track and locate devices in
indoor and outdoor environments, or even worldwide in some
examples. In this way, the present embodiments may provide
techniques to efficiently determine and track the approximate
geolocation of electronic devices within or about indoor and
outdoor environments, or otherwise in any of various environments
in which large-scale satellite systems may be inaccurate,
imprecise, or otherwise unavailable.
[0027] FIG. 1A illustrates an example system 100 that supports the
PubSub communication pattern. Publisher clients (e.g., Publisher 1)
can publish messages to named channels (e.g., "Channel 1") by way
of the system 100. A message can comprise any type of information
including one or more of the following: text, image content, sound
content, multimedia content, video content, binary data, and so on.
Other types of message data are possible. Subscriber clients (e.g.,
Subscriber 2) can subscribe to a named channel using the system 100
and start receiving messages which occur after the subscription
request or from a given position (e.g., a message number or time
offset). A client can be both a publisher and a subscriber.
[0028] Depending on the configuration, a PubSub system can be
categorized as follows: [0029] One to One (1:1). In this
configuration there is one publisher and one subscriber per
channel. A typical use case is private messaging. [0030] One to
Many (1:N). In this configuration there is one publisher and
multiple subscribers per channel. Typical use cases are
broadcasting messages (e.g., stock prices). [0031] Many to Many
(M:N). In this configuration there are many publishers publishing
to a single channel. The messages are then delivered to multiple
subscribers. Typical use cases are map applications.
[0032] There is no separate operation needed to create a named
channel. A channel is created implicitly when the channel is
subscribed to or when a message is published to the channel. In
some implementations, channel names can be qualified by a name
space. A name space comprises one or more channel names. Different
name spaces can have the same channel names without causing
ambiguity. The name space name can be a prefix of a channel name
where the name space and channel name are separated by a dot or
other suitable separator. In some implementations, name spaces can
be used when specifying channel authorization settings. For
instance, the messaging system 100 may have app1.foo and
app1.system.notifications channels where "app1" is the name of the
name space. The system can allow clients to subscribe and publish
to the app1.foo channel. However, clients can only subscribe to,
but not publish to the app1.system.notifications channel.
[0033] FIG. 1B illustrates functional layers of software on an
example client device. A client device (e.g., client 102) is a data
processing apparatus such as, for example, a personal computer, a
laptop computer, a tablet computer, a smart phone, a smart watch,
or a server computer. Other types of client devices are possible.
The application layer 104 comprises the end-user application(s)
that will integrate with the PubSub system 100. The messaging layer
106 is a programmatic interface for the application layer 104 to
utilize services of the system 100 such as channel subscription,
message publication, message retrieval, user authentication, and
user authorization. In some implementations, the messages passed to
and from the messaging layer 106 are encoded as JavaScript Object
Notation (JSON) objects. Other message encoding schemes are
possible.
[0034] The operating system 108 layer comprises the operating
system software on the client 102. In various implementations,
messages can be sent and received to/from the system 100 using
persistent or non-persistent connections. Persistent connections
can be created using, for example, network sockets. A transport
protocol such as TCP/IP layer 112 implements the Transport Control
Protocol/Internet Protocol communication with the system 100 that
can be used by the messaging layer 106 to send messages over
connections to the system 100. Other communication protocols are
possible including, for example, User Datagram Protocol (UDP). In
further implementations, an optional Transport Layer Security (TLS)
layer 110 can be employed to ensure the confidentiality of the
messages.
[0035] FIG. 2 is a diagram of an example messaging system 100. The
system 100 provides functionality for implementing PubSub
communication patterns. The system comprises software components
and storage that can be deployed at one or more data centers 122 in
one or more geographic locations, for example. The system comprises
MX nodes (e.g., MX nodes or multiplexer nodes 202, 204 and 206), Q
nodes (e.g., Q nodes or queue nodes 208, 210 and 212), one or more
configuration manager nodes (e.g., configuration manager 214), and
optionally one or more C nodes (e.g., C nodes or cache nodes 220
and 222). Each node can execute in a virtual machine or on a
physical machine (e.g., a data processing apparatus). Each MX node
can serve as a termination point for one or more publisher and/or
subscriber connections through the external network 216. The
internal communication among MX nodes, Q nodes, C nodes, and the
configuration manager can be conducted over an internal network
218, for example. By way of illustration, MX node 204 can be the
terminus of a subscriber connection from client 102. Each Q node
buffers channel data for consumption by the MX nodes. An ordered
sequence of messages published to a channel is a logical channel
stream. For example, if three clients publish messages to a given
channel, the combined messages published by the clients comprise a
channel stream. Messages can be ordered in a channel stream, for
example, by time of publication by the client, by time of receipt
by an MX node, or by time of receipt by a Q node. Other ways for
ordering messages in a channel stream are possible. In the case
where more than one message would be assigned to the same position
in the order, one of the messages can be chosen (e.g., randomly) to
have a later sequence in the order. Each configuration manager node
is responsible for managing Q node load, for example, by assigning
channels to Q nodes and/or splitting channel streams into so-called
streamlets. Streamlets are discussed further below. The optional C
nodes provide caching and load removal from the Q nodes.
[0036] In the example messaging system 100, one or more client
devices (publishers and/or subscribers) establish respective
persistent connections (e.g., TCP connections) to an MX node (e.g.,
MX node 204). The MX node serves as a termination point for these
connections. For instance, external messages (e.g., between
respective client devices and the MX node) carried by these
connections can be encoded based on an external protocol (e.g.,
JSON). The MX node terminates the external protocol and translates
the external messages to internal communication, and vice versa.
The MX nodes publish and subscribe to streamlets on behalf of
clients. In this way, an MX node can multiplex and merge requests
of client devices subscribing for or publishing to the same
channel, thus representing multiple client devices as one, instead
of one by one.
[0037] In the example messaging system 100, a Q node (e.g., Q node
208) can store one or more streamlets of one or more channel
streams. A streamlet is a data buffer for a portion of a channel
stream. A streamlet will close to writing when its storage is full.
A streamlet will close to reading and writing and be de-allocated
when its time-to-live (TTL) has expired. By way of illustration, a
streamlet can have a maximum size of 1 MB and a TTL of three
minutes. Different channels can have streamlets limited by
different sizes and/or by different TTLs. For example, streamlets
in one channel can exist for up to three minutes, while streamlets
in another channel can exist for up to 10 minutes. In various
implementations, a streamlet corresponds to a computing process
running on a Q node. The computing process can be terminated after
the streamlet's TTL has expired, thus freeing up computing
resources (for the streamlet) back to the Q node, for example.
[0038] When receiving a publish request from a client device, an MX
node (e.g., MX node 204) makes a request to a configuration manager
(e.g., configuration manager 214) to grant access to a streamlet to
write the message being published. Note, however, that if the MX
node has already been granted write access to a streamlet for the
channel (and the channel has not been closed to writing), the MX
node can write the message to that streamlet without having to
request a grant to access the streamlet. Once a message is written
to a streamlet for a channel, the message can be read by MX nodes
and provided to subscribers of that channel.
[0039] Similarly, when receiving a channel subscription request
from a client device, an MX node makes a request to a configuration
manager to grant access to a streamlet for the channel from which
messages are read. If the MX node has already been granted read
access to a streamlet for the channel (and the channel's TTL has
not been closed to reading), the MX node can read messages from the
streamlet without having to request a grant to access the
streamlet. The read messages can then be forwarded to client
devices that have subscribed to the channel. In various
implementations, messages read from streamlets are cached by MX
nodes so that MX nodes can reduce the number of times needed to
read from the streamlets.
[0040] By way of illustration, an MX node can request a grant from
the configuration manager that allows the MX node to store a block
of data into a streamlet on a particular Q node that stores
streamlets of the particular channel. Example streamlet grant
request and grant data structures are as follows:
TABLE-US-00001 StreamletGrantRequest = { "channel": string( )
"mode": "read" | "write" "position": 0 } StreamletGrantResponse = {
"streamlet-id": "abcdef82734987", "limit-size": 2000000, # 2
megabytes max "limit-msgs": 5000, # 5 thousand messages max
"limit-life": 4000, # the grant is valid for 4 seconds "q-node":
string( ) "position": 0 }
[0041] The StreamletGrantRequest data structure stores the name of
the stream channel and a mode indicating whether the MX node
intends on reading from or writing to the streamlet. The MX node
sends the StreamletGrantRequest to a configuration manager node.
The configuration manager node, in response, sends the MX node a
StreamletGrantResponse data structure. The StreamletGrantResponse
contains an identifier of the streamlet (streamlet-id), the maximum
size of the streamlet (limit-size), the maximum number of messages
that the streamlet can store (limit-msgs), the TTL (limit-life),
and an identifier of a Q node (q-node) on which the streamlet
resides. The StreamletGrantRequest and StreamletGrantResponse can
also have a position field that points to a position in a streamlet
(or a position in a channel) for reading from the streamlet.
[0042] A grant becomes invalid once the streamlet has closed. For
example, a streamlet is closed to reading and writing once the
streamlet's TTL has expired and a streamlet is closed to writing
when the streamlet's storage is full. When a grant becomes invalid,
the MX node can request a new grant from the configuration manager
to read from or write to a streamlet. The new grant will reference
a different streamlet and will refer to the same or a different Q
node depending on where the new streamlet resides.
[0043] FIG. 3A is a data flow diagram of an example method for
writing data to a streamlet in various embodiments. In FIG. 3A,
when an MX node (e.g., MX node 202) request to write to a streamlet
is granted by a configuration manager (e.g., configuration manager
214), as described before, the MX node establishes a Transmission
Control Protocol (TCP) connection with the Q node (e.g., Q node
208) identified in the grant response received from the
configuration manager (302). A streamlet can be written
concurrently by multiple write grants (e.g., for messages published
by multiple publisher clients). Other types of connection protocols
between the MX node and the Q node are possible.
[0044] The MX node then sends a prepare-publish message with an
identifier of a streamlet that the MX node wants to write to the Q
node (304). The streamlet identifier and Q node identifier can be
provided by the configuration manager in the write grant as
described earlier. The Q node hands over the message to a handler
process 301 (e.g., a computing process running on the Q node) for
the identified streamlet (306). The handler process can send to the
MX node an acknowledgement (308). After receiving the
acknowledgement, the MX node starts writing (publishing) messages
(e.g., 310, 312, 314, and 318) to the handler process, which in
turn stores the received data in the identified streamlet. The
handler process can also send acknowledgements (316, 320) to the MX
node for the received data. In some implementations,
acknowledgements can be piggy-backed or cumulative. For example,
the handler process can send to the MX node an acknowledgement for
each predetermined amount of data received (e.g., for every 100
messages received) or for every predetermined time period (e.g.,
for every one millisecond). Other acknowledgement scheduling
algorithms, such as Nagle's algorithm, can be used.
[0045] If the streamlet can no longer accept published data (e.g.,
when the streamlet is full), the handler process sends a
Negative-Acknowledgement (NAK) message (330) indicating a problem,
following by an EOF (end-of-file) message (332). In this way, the
handler process closes the association with the MX node for the
publish grant. The MX node can then request a write grant for
another streamlet from a configuration manager if the MX node has
additional messages to store.
[0046] FIG. 3B is a data flow diagram of an example method for
reading data from a streamlet in various embodiments. In FIG. 3B,
an MX node (e.g., MX node 204) sends to a configuration manager
(e.g., configuration manager 214) a request for reading a
particular channel starting from a particular message or time
offset in the channel. The configuration manager returns to the MX
node a read grant including an identifier of a streamlet containing
the particular message, a position in the streamlet corresponding
to the particular message, and an identifier of a Q node (e.g., Q
node 208) containing the particular streamlet. The MX node then
establishes a TCP connection with the Q node (352). Other types of
connection protocols between the MX node and the Q node are
possible.
[0047] The MX node then sends to the Q node a subscribe message
(354) with the identifier of the streamlet (in the Q node) and the
position in the streamlet from which the MX node wants to read
(356). The Q node hands over the subscribe message to a handler
process 351 for the streamlet (356). The handler process can send
to the MX node an acknowledgement (358). The handler process then
sends messages (360, 364, 366), starting at the position in the
streamlet, to the MX node. In some implementations, the handler
process can send all of the messages in the streamlet to the MX
node. After sending the last message in a particular streamlet, the
handler process can send a notification of the last message to the
MX node. The MX node can send to the configuration manager another
request for another streamlet containing a next message in the
particular channel.
[0048] If the particular streamlet is closed (e.g., after its TTL
has expired), the handler process can send an unsubscribe message
(390), followed by an EOF message (392), to close the association
with the MX node for the read grant. The MX node can close the
association with the handler process when the MX node moves to
another streamlet for messages in the particular channel (e.g., as
instructed by the configuration manager). The MX node can also
close the association with the handler process if the MX node
receives an unsubscribe message from a corresponding client
device.
[0049] In various implementations, a streamlet can be written into
and read from at the same time instance. For example, there can be
a valid read grant and a valid write grant at the same time
instance. In various implementations, a streamlet can be read
concurrently by multiple read grants (e.g., for channels subscribed
to by multiple publisher clients). The handler process of the
streamlet can order messages from concurrent write grants based on,
for example, time-of-arrival, and store the messages based on the
order. In this way, messages published to a channel from multiple
publisher clients can be serialized and stored in a streamlet of
the channel.
[0050] In the messaging system 100, one or more C nodes (e.g., C
node 220) can offload data transfers from one or more Q nodes. For
instance, if there are many MX nodes requesting streamlets from Q
nodes for a particular channel, the streamlets can be offloaded and
cached in one or more C nodes. The MX nodes (e.g., as instructed by
read grants from a configuration manager) can read the streamlets
from the C nodes instead.
[0051] As described above, messages for a channel in the messaging
system 100 are ordered in a channel stream. A configuration manager
(e.g., configuration manager 214) splits the channel stream into
fixed-sized streamlets that each reside on a respective Q node. In
this way, storing a channel stream can be shared among many Q
nodes; each Q node stores a portion (one or more streamlets) of the
channel stream. More particularly, a streamlet can be stored in,
for example, registers and/or dynamic memory elements associated
with a computing process on a Q node, thus avoiding the need to
access persistent, slower storage devices such as hard disks. This
results in faster message access. The configuration manager can
also balance load among Q nodes in the messaging system 100 by
monitoring respective workloads of the Q nodes and allocating
streamlets in a way that avoids overloading any one Q node.
[0052] In various implementations, a configuration manager
maintains a list identifying each active streamlet, the respective
Q node on which the streamlet resides, an identification of the
position of the first message in the streamlet, and whether the
streamlet is closed for writing. In some implementations, Q nodes
notify the configuration manager and/or any MX nodes that are
publishing to a streamlet that the streamlet is closed due to being
full or when the streamlet's TTL has expired. When a streamlet is
closed, the streamlet remains on the configuration manager's list
of active streamlets until the streamlet's TTL has expired so that
MX nodes can continue to retrieve messages from the streamlet.
[0053] When an MX node requests a write grant for a given channel
and there is not a streamlet for the channel that can be written
to, the configuration manager allocates a new streamlet on one of
the Q nodes and returns the identity of the streamlet and the Q
node in the StreamletGrantResponse. Otherwise, the configuration
manager returns the identity of the currently open for writing
streamlet and corresponding Q node in the StreamletGrantResponse.
MX nodes can publish messages to the streamlet until the streamlet
is full or the streamlet's TTL has expired, after which a new
streamlet can be allocated by the configuration manager.
[0054] When an MX node requests a read grant for a given channel
and there is not a streamlet for the channel that can be read from,
the configuration manager allocates a new streamlet on one of the Q
nodes and returns the identity of the streamlet and the Q node in
the StreamletGrantResponse. Otherwise, the configuration manager
returns the identity of the streamlet and Q node that contains the
position from which the MX node wishes to read. The Q node can then
begin sending messages to the MX node from the streamlet beginning
at the specified position until there are no more messages in the
streamlet to send. When a new message is published to a streamlet,
MX nodes that have subscribed to that streamlet will receive the
new message. If a streamlet's TTL has expired, the handler process
351 can send an EOF message (392) to any MX nodes that are
subscribed to the streamlet.
[0055] In some implementations, the messaging system 100 can
include multiple configuration managers (e.g., configuration
manager 214 plus one or more other configuration managers).
Multiple configuration managers can provide resiliency and prevent
single point of failure. For instance, one configuration manager
can replicate lists of streamlets and current grants it maintains
to another "slave" configuration manager. As another example,
multiple configuration managers can coordinate operations between
them using distributed consensus protocols, such as, for example,
Paxos or Raft protocols.
[0056] FIG. 4A is a data flow diagram of an example method for
publishing messages to a channel of a messaging system. In FIG. 4A,
publishers (e.g., publisher clients 402, 404, 406) publish messages
to the messaging system 100 described earlier in reference to FIG.
2. For instance, publishers 402 respectively establish connections
411 and send publish requests to the MX node 202. Publishers 404
respectively establish connections 413 and send publish requests to
the MX node 206. Publishers 406 respectively establish connections
415 and send publish requests to the MX node 204. Here, the MX
nodes can communicate (417) with a configuration manager (e.g.,
configuration manager 214) and one or more Q nodes (e.g., Q nodes
212 and 208) in the messaging system 100 via the internal network
218.
[0057] By way of illustration, each publish request (e.g., in JSON
key/value pairs) from a publisher to an MX node includes a channel
name and a message. The MX node (e.g., MX node 202) can assign the
message in the publish request to a distinct channel in the
messaging system 100 based on the channel name (e.g., "foo") of the
publish request. The MX node can confirm the assigned channel with
the configuration manager 214. If the channel (specified in the
subscribe request) does not yet exist in the messaging system 100,
the configuration manager can create and maintain a new channel in
the messaging system 100. For instance, the configuration manager
can maintain a new channel by maintaining a list identifying each
active streamlet of the channel's stream, the respective Q node on
which the streamlet resides, and identification of the positions of
the first and last messages in the streamlet as described
earlier.
[0058] For messages of a particular channel, the MX node can store
the messages in one or more buffers or streamlets in the messaging
system 100. For instance, the MX node 202 receives from the
publishers 402 requests to publish messages M11, M12, M13, and M14
to a channel foo. The MX node 206 receives from the publishers 404
requests to publish messages M78 and M79 to the channel foo. The MX
node 204 receives from the publishers 406 requests to publish
messages M26, M27, M28, M29, M30, and M31 to the channel foo.
[0059] The MX nodes can identify one or more streamlets for storing
messages for the channel foo. As described earlier, each MX node
can request a write grant from the configuration manager 214 that
allows the MX node to store the messages in a streamlet of the
channel foo. For instance, the MX node 202 receives a grant from
the configuration manager 214 to write messages M11, M12, M13, and
M14 to a streamlet 4101 on the Q node 212. The MX node 206 receives
a grant from the configuration manager 214 to write messages M78
and M79 to the streamlet 4101. Here, the streamlet 4101 is the last
one (at the moment) of a sequence of streamlets of the channel
stream 430 storing messages of the channel foo. The streamlet 4101
has messages (421) of the channel foo that were previously stored
in the streamlet 4101, but is still open, i.e., the streamlet 4101
still has space for storing more messages and the streamlet's TTL
has not expired.
[0060] The MX node 202 can arrange the messages for the channel foo
based on the respective time that each message was received by the
MX node 202, e.g., M11, M13, M14, M12 (422), and store the received
messages as arranged in the streamlet 4101. That is, the MX node
202 receives M11 first, followed by M13, M14, and M12. Similarly,
the MX node 206 can arrange the messages for the channel foo based
on their respective time that each message was received by the MX
node 206, e.g., M78, M79 (423), and store the received messages as
arranged in the streamlet 4101. Other arrangements or ordering of
the messages for the channel are possible.
[0061] The MX node 202 (or MX node 206) can store the received
messages using the method for writing data to a streamlet described
earlier in reference to FIG. 3A, for example. In various
implementations, the MX node 202 (or MX node 206) can buffer (e.g.,
in a local data buffer) the received messages for the channel foo
and store the received messages in a streamlet for the channel foo
(e.g., streamlet 4101) when the buffered messages reach a
predetermined number or size (e.g., 100 messages) or when a
predetermined time (e.g., 50 milliseconds) has elapsed. For
instance, the MX node 202 can store in the streamlet 100 messages
at a time or in every 50 milliseconds. Other appropriate algorithms
and techniques, such as Nagle's algorithm, can be used for managing
the buffered messages.
[0062] In various implementations, the Q node 212 (e.g., a handler
process) stores the messages of the channel foo in the streamlet
4101 in the order as arranged by the MX node 202 and MX node 206.
The Q node 212 stores the messages of the channel foo in the
streamlet 4101 in the order the Q node 212 receives the messages.
For instance, assume that the Q node 212 receives messages M78
(from the MX node 206) first, followed by messages M11 and M13
(from the MX node 202), M79 (from the MX node 206), and M14 and M12
(from the MX node 202). The Q node 212 stores in the streamlet 4101
the messages in the order as received, e.g., M78, M11, M13, M79,
M14, and M12, immediately after the messages 421 that are already
stored in the streamlet 4101. In this way, messages published to
the channel foo from multiple publishers (e.g., 402, 404) can be
serialized in a particular order and stored in the streamlet 4101
of the channel foo. Different subscribers that subscribe to the
channel foo will receive messages of the channel foo in the same
particular order, as will be described in more detail in reference
to FIG. 4B.
[0063] In the example of FIG. 4A, at a time instance after the
message M12 was stored in the streamlet 4101, the MX node 204
requests a grant from the configuration manager 214 to write to the
channel foo. The configuration manager 214 provides the MX node 204
a grant to write messages to the streamlet 4101, as the streamlet
4101 is still open for writing. The MX node 204 arranges the
messages for the channel foo based on the respective time that each
message was received by the MX node 204, e.g., M26, M27, M31, M29,
M30, M28 (424), and stores the messages as arranged for the channel
foo.
[0064] By way of illustration, assume that the message M26 is
stored to the last available position of the streamlet 4101. As the
streamlet 4101 is now full, the Q node 212 sends to the MX node 204
a NAK message, following by an EOF message, to close the
association with the MX node 204 for the write grant, as described
earlier in reference to FIG. 3A. The MX node 204 then requests
another write grant from the configuration manager 214 for
additional messages (e.g., M27, M31, and so on) for the channel
foo.
[0065] The configuration manager 214 can monitor available Q nodes
in the messaging system 100 for their respective workloads (e.g.,
how many streamlets are residing in each Q node). The configuration
manager 214 can allocate a streamlet for the write request from the
MX node 204 such that overloading (e.g., too many streamlets or too
many read or write grants) can be avoided for any given Q node. For
instance, the configuration manager 214 can identify a least loaded
Q node in the messaging system 100 and allocate a new streamlet on
the least loaded Q node for write requests from the MX node 204. In
the example of FIG. 4A, the configuration manager 214 allocates a
new streamlet 4102 on the Q node 208 and provides a write grant to
the MX node 204 to write messages for the channel foo to the
streamlet 4102. As shown in FIG. 4A, the Q node stores in the
streamlet 4102 the messages from the MX node 204 in an order as
arranged by the MX node 204: M27, M31, M29, M30, and M28 (assuming
that there is no other concurrent write grant for the streamlet
4102 at the moment).
[0066] When the configuration manager 214 allocates a new streamlet
(e.g., streamlet 4102) for a request for a grant from an MX node
(e.g., MX node 204) to write to a channel (e.g., foo), the
configuration manager 214 assigns to the streamlet its TTL, which
will expire after TTLs of other streamlets that are already in the
channel's stream. For instance, the configuration manager 214 can
assign to each streamlet of the channel foo's channel stream a TTL
of 3 minutes when allocating the streamlet. That is, each streamlet
will expire 3 minutes after it is allocated (created) by the
configuration manager 214. Since a new streamlet is allocated after
a previous streamlet is closed (e.g., filled entirely or expired),
in this way, the channel foo's channel stream comprises streamlets
that each expires sequentially after its previous streamlet
expires. For instance, as shown in an example channel stream 430 of
the channel foo in FIG. 4A, streamlet 4098 and streamlets before
4098 have expired (as indicated by the dotted-lined gray-out
boxes). Messages stored in these expired streamlets are not
available for reading for subscribers of the channel foo.
Streamlets 4099, 4100, 4101, and 4102 are still active (not
expired). The streamlets 4099, 4100, and 4101 are closed for
writing, but still are available for reading. The streamlet 4102 is
available for reading and writing, at the moment when the message
M28 was stored in the streamlet 4102. At a later time, the
streamlet 4099 will expire, following by the streamlets 4100, 4101,
and so on.
[0067] FIG. 4B is a data flow diagram of an example method for
subscribing to a channel of a messaging system. In FIG. 4B, a
subscriber 480 establishes a connection 462 with an MX node 461 of
the messaging system 100. Subscriber 482 establishes a connection
463 with the MX node 461. Subscriber 485 establishes a connection
467 with an MX node 468 of the messaging system 100. Here, the MX
nodes 461 and 468 can respectively communicate (464) with the
configuration manager 214 and one or more Q nodes in the messaging
system 100 via the internal network 218.
[0068] A subscriber (e.g., subscriber 480) can subscribe to the
channel foo of the messaging system 100 by establishing a
connection (e.g., 462) and sending a request for subscribing to
messages of the channel foo to an MX node (e.g., MX node 461). The
request (e.g., in JSON key/value pairs) can include a channel name,
such as, for example, "foo." When receiving the subscribe request,
the MX node 461 can send to the configuration manager 214 a request
for a read grant for a streamlet in the channel foo's channel
stream.
[0069] By way of illustration, assume that at the current moment
the channel foo's channel stream 431 includes active streamlets
4102, 4103, and 4104, as shown in FIG. 4B. The streamlets 4102 and
4103 each are full. The streamlet 4104 stores messages of the
channel foo, including the last message (at the current moment)
stored at a position 47731. Streamlets 4101 and streamlets before
4101 are invalid, as their respective TTLs have expired. Note that
the messages M78, M11, M13, M79, M14, M12, and M26 stored in the
streamlet 4101, described earlier in reference to FIG. 4A, are no
longer available for subscribers of the channel foo, since the
streamlet 4101 is no longer valid, as its TTL has expired. As
described earlier, each streamlet in the channel foo's channel
stream has a TTL of 3 minutes, thus only messages (as stored in
streamlets of the channel foo) that are published to the channel
foo (i.e., stored into the channel's streamlets) no earlier than 3
minutes from the current time can be available for subscribers of
the channel foo.
[0070] The MX node 461 can request a read grant for all available
messages in the channel foo, for example, when the subscriber 480
is a new subscriber to the channel foo. Based on the request, the
configuration manager 214 provides the MX node 461 a read grant to
the streamlet 4102 (on the Q node 208) that is the earliest
streamlet in the active streamlets of the channel foo (i.e., the
first in the sequence of the active streamlets). The MX node 461
can retrieve messages in the streamlet 4102 from the Q node 208,
using the method for reading data from a streamlet described
earlier in reference to FIG. 3B, for example. Note that the
messages retrieved from the streamlet 4102 maintain the same order
as stored in the streamlet 4102. However, other arrangements or
ordering of the messages in the streamlet are possible. In various
implementations, when providing messages stored in the streamlet
4102 to the MX node 461, the Q node 208 can buffer (e.g., in a
local data buffer) the messages and send the messages to the MX
node 461 when the buffer messages reach a predetermined number or
size (e.g., 200 messages) or a predetermined time (e.g., 50
milliseconds) has elapsed. For instance, the Q node 208 can send
the channel foo's messages (from the streamlet 4102) to the MX node
461 200 messages at a time or in every 50 milliseconds. Other
appropriate algorithms and techniques, such as Nagle's algorithm,
can be used for managing the buffered messages.
[0071] After receiving the last message in the streamlet 4102, the
MX node 461 can send an acknowledgement to the Q node 208, and send
to the configuration manager 214 another request (e.g., for a read
grant) for the next streamlet in the channel stream of the channel
foo. Based on the request, the configuration manager 214 provides
the MX node 461 a read grant to the streamlet 4103 (on Q node 472)
that logically follows the streamlet 4102 in the sequence of active
streamlets of the channel foo. The MX node 461 can retrieve
messages stored in the streamlet 4103, e.g., using the method for
reading data from a streamlet described earlier in reference to
FIG. 3B, until it retrieves the last message stored in the
streamlet 4103. The MX node 461 can send to the configuration
manager 214 yet another request for a read grant for messages in
the next streamlet 4104 (on Q node 474). After receiving the read
grant, the MX node 461 retrieves messages of the channel foo stored
in the streamlet 4104, until the last message at the position
47731. Similarly, the MX node 468 can retrieve messages from the
streamlets 4102, 4103, and 4104 (as shown with dotted arrows in
FIG. 4B), and provide the messages to the subscriber 485.
[0072] The MX node 461 can send the retrieved messages of the
channel foo to the subscriber 480 (via the connection 462) while
receiving the messages from the Q nodes 208, 472, or 474. In
various implementations, the MX node 461 can store the retrieved
messages in a local buffer. In this way, the retrieved messages can
be provided to another subscriber (e.g., subscriber 482) when the
other subscriber subscribes to the channel foo and requests the
channel's messages. The MX node 461 can remove messages stored in
the local buffer that each has a time of publication that has
exceeded a predetermined time period. For instance, the MX node 461
can remove messages (stored in the local buffer) with respective
times of publication exceeding 3 minutes. In some implementations,
the predetermined time period for keeping messages in the local
buffer on MX node 461 can be the same as or similar to the
time-to-live duration of a streamlet in the channel foo's channel
stream, since at a given moment, messages retrieved from the
channel's stream do not include those in streamlets having
respective times-to-live that had already expired.
[0073] The messages retrieved from the channel stream 431 and sent
to the subscriber 480 (by the MX node 461) are arranged in the same
order as the messages were stored in the channel stream, although
other arrangements or ordering of the messages are possible. For
instance, messages published to the channel foo are serialized and
stored in the streamlet 4102 in a particular order (e.g., M27, M31,
M29, M30, and so on), then stored subsequently in the streamlet
4103 and the streamlet 4104. The MX node retrieves messages from
the channel stream 431 and provides the retrieved messages to the
subscriber 480 in the same order as the messages are stored in the
channel stream: M27, M31, M29, M30, and so on, followed by ordered
messages in the streamlet 4103, and followed by ordered messages in
the streamlet 4104.
[0074] Instead of retrieving all available messages in the channel
stream 431, the MX node 461 can request a read grant for messages
stored in the channel stream 431 starting from a message at
particular position, e.g., position 47202. For instance, the
position 47202 can correspond to an earlier time instance (e.g., 10
seconds before the current time) when the subscriber 480 was last
subscribing to the channel foo (e.g., via a connection to the MX
node 461 or another MX node of the messaging system 100). The MX
node 461 can send to the configuration manager 214 a request for a
read grant for messages starting at the position 47202. Based on
the request, the configuration manager 214 provides the MX node 461
a read grant to the streamlet 4104 (on the Q node 474) and a
position on the streamlet 4104 that corresponds to the channel
stream position 47202. The MX node 461 can retrieve messages in the
streamlet 4104 starting from the provided position, and send the
retrieved messages to the subscriber 480.
[0075] As described above in reference to FIGS. 4A and 4B, messages
published to the channel foo are serialized and stored in the
channel's streamlets in a particular order. The configuration
manager 214 maintains the ordered sequence of streamlets as they
are created throughout their respective times-to-live. Messages
retrieved from the streamlets by an MX node (e.g., MX node 461, or
MX node 468) and provided to a subscriber can be, in some
implementations, in the same order as the messages are stored in
the ordered sequence of streamlets. In this way, messages sent to
different subscribers (e.g., subscriber 480, subscriber 482, or
subscriber 485) can be in the same order (as the messages are
stored in the streamlets), regardless which MX nodes the
subscribers are connected to.
[0076] In various implementations, a streamlet stores messages in a
set of blocks of messages. Each block stores a number of messages.
For instance, a block can store two hundred kilobytes of messages
(although other sizes of blocks of messages are possible). Each
block has its own time-to-live, which can be shorter than the
time-to-live of the streamlet holding the block. Once a block's TTL
has expired, the block can be discarded from the streamlet holding
the block, as described in more detail below in reference to FIG.
4C.
[0077] FIG. 4C is an example data structure for storing messages of
a channel of a messaging system. As described with the channel foo
in reference to FIGS. 4A and 4B, assume that at the current moment
the channel foo's channel stream 432 includes active streamlets
4104 and 4105, as shown in FIG. 4C. Streamlet 4103 and streamlets
before 4103 are invalid, as their respective TTLs have expired. The
streamlet 4104 is already full for its capacity (e.g., as
determined by a corresponding write grant) and is closed for
additional message writes. The streamlet 4104 is still available
for message reads. The streamlet 4105 is open and is available for
message writes and reads.
[0078] By way of illustration, the streamlet 4104 (e.g., a
computing process running on the Q node 474 shown in FIG. 4B)
currently holds two blocks of messages. Block 494 holds messages
from channel positions 47301 to 47850. Block 495 holds messages
from channel positions 47851 to 48000. The streamlet 4105 (e.g., a
computing process running on another Q node in the messaging system
100) currently holds two blocks of messages. Block 496 holds
messages from channel positions 48001 to 48200. Block 497 holds
messages starting from channel position 48201, and still accepts
additional messages of the channel foo.
[0079] When the streamlet 4104 was created (e.g., by a write
grant), a first block (sub-buffer) 492 was created to store
messages, e.g., from channel positions 47010 to 47100. Later on,
after the block 492 had reached its capacity, another block 493 was
created to store messages, e.g., from channel positions 47111 to
47300. Blocks 494 and 495 were subsequently created to store
additional messages. Afterwards, the streamlet 4104 was closed for
additional message writes, and the streamlet 4105 was created with
additional blocks for storing additional messages of the channel
foo.
[0080] In this example, the respective TTL's of blocks 492 and 493
had expired. The messages stored in these two blocks (from channel
positions 47010 to 47300) are no longer available for reading by
subscribers of the channel foo. The streamlet 4104 can discard
these two expired blocks, e.g., by de-allocating the memory space
for the blocks 492 and 493. The blocks 494 or 495 could become
expired and be discarded by the streamlet 4104, before the
streamlet 4104 itself becomes invalid. Alternatively, streamlet
4104 itself could become invalid before the blocks 494 or 495
become expired. In this way, a streamlet can hold one or more
blocks of messages, or contain no block of messages, depending on
respective TTLs of the streamlet and blocks, for example.
[0081] A streamlet, or a computing process running on a Q node in
the messaging system 100, can create a block for storing messages
of a channel by allocating a certain size of memory space from the
Q node. The streamlet can receive, from an MX node in the messaging
system 100, one message at a time and store the received message in
the block. Alternatively, the MX node can assemble (i.e., buffer) a
group of messages and send the group of messages to the Q node. The
streamlet can allocate a block of memory space (from the Q node)
and store the group of messages in the block. The MX node can also
perform compression on the group of messages, e.g., by removing a
common header from each message or performing other suitable
compression techniques.
[0082] As described above, a streamlet (a data buffer) residing on
a Q node stores messages of a channel in the messaging system 100.
To prevent failure of the Q node (a single point failure) that can
cause messages being lost, the messaging system 100 can replicate
messages on multiple Q nodes, as described in more detail
below.
[0083] FIG. 5A is a data flow diagram of an example method 500 for
publishing and replicating messages of the messaging system 100. As
described earlier in reference to FIG. 4A, the MX node 204 receives
messages (of the channel foo) from the publishers 406. The
configuration manager 214 can instruct the MX Node 204 (e.g., with
a write grant) to store the messages in the streamlet 4102 on the Q
node 208. In FIG. 5A, instead of storing the messages on a single
node (e.g., Q node 208), the configuration manager 214 allocates
multiple Q nodes to store multiple copies of the streamlet 4102 on
these Q nodes.
[0084] By way of illustration, the configuration manager 214
allocates Q nodes 208, 502, 504, and 506 in the messaging system
100 to store copies of the streamlet 4102. The configuration
manager 214 instructs the MX node 204 to transmit the messages for
the channel foo (e.g., messages M27, M31, M29, M30, and M28) to the
Q node 208 (512). A computing process running on the Q node 208
stores the messages in the first copy (copy #1) of the streamlet
4102. Instead of sending an acknowledgement message to the MX node
204 after storing the messages, the Q node 208 forwards the
messages to the Q node 502 (514). A computing process running on
the Q node 502 stores the messages in another copy (copy #2) of the
streamlet 4102. Meanwhile, the Q node 502 forwards the messages to
the Q node 504 (516). A computing process running on the Q node 504
stores the messages in yet another copy (copy #3) of the streamlet
4102. The Q node 504 also forwards the message to the Q node 506
(518). A computing process running on the Q node 506 stores the
messages in yet another copy (copy #4) of the streamlet 4102. The Q
node 506 can send an acknowledgement message to the MX node 204,
indicating that all the messages (M27, M31, M29, M30, and M28) have
been stored successfully in streamlet copies #1, #2, #3 and #4.
[0085] In some implementations, after successfully storing the last
copy (copy #4), the Q node 506 can send an acknowledgement to its
upstream Q node (504), which in turns sends an acknowledgement to
its upstream Q node (502), and so on, until the acknowledgement is
sent to the Q node 208 storing the first copy (copy #1). The Q node
208 can send an acknowledgement message to the MX node 204,
indicating that all messages have been stored successfully in the
streamlet 4102 (i.e., in the copies #1, #2, #3 and #4).
[0086] In this way, four copies of the streamlet 4102 (and each
message in the streamlet) are stored in four different Q nodes.
Other numbers (e.g., two, three, five, or other suitable number) of
copies of a streamlet are also possible. In the present
illustration, the four copies form a chain of copies including a
head copy in the copy #1 and a tail copy in the copy #4. When a new
message is published to the streamlet 4102, the message is first
stored in the head copy (copy #1) on the Q node 208. The message is
then forwarded downstream to the next adjacent copy, the copy #2 on
the Q node 502 for storage, then to the copy #3 on the Q node 504
for storage, until the message is stored in the tail copy the copy
#4 on the Q node 506.
[0087] In addition to storing and forwarding by messages, the
computing processes running on Q nodes that store copies of a
streamlet can also store and forward messages by blocks of
messages, as described earlier in reference to FIG. 4C. For
instance, the computing process storing the copy #1 of the
streamlet 4102 on Q node 208 can allocate memory and store a block
of, for example, 200 kilobytes of messages (although other sizes of
blocks of messages are possible), and forward the block of messages
to the next adjacent copy (copy #2) of the chain for storage, and
so on, until the block messages is stored in the tail copy (copy
#4) on the Q node 506.
[0088] Messages of the streamlet 4102 can be retrieved and
delivered to a subscriber of the channel foo from one of the copies
of the streamlet 4102. FIG. 5B is a data flow diagram of an example
method 550 for retrieving stored messages in the messaging system
100. For instance, the subscriber 480 can send a request for
subscribing to messages of the channel to the MX node 461, as
described earlier in reference to FIG. 4B. The configuration
manager 214 can provide to the MX node 461 a read grant for one of
the copies of the streamlet 4102. The MX node 461 can retrieve
messages of the streamlet 4102 from one of the Q nodes storing a
copy of the streamlet 4102, and provide the retrieved messages to
the subscriber 480. For instance, the MX node 461 can retrieve
messages from the copy #4 (the tail copy) stored on the Q node 506
(522). As for another example, the MX node 461 can retrieve
messages from the copy #2 stored on the Q node 502 (524). In this
way, the multiple copies of a streamlet (e.g., copies #1, #2, #3,
and #4 of the streamlet 4102) provide replication and redundancy
against failure if only one copy of the streamlet were stored in
the messaging system 100. In various implementations, the
configuration manager 214 can balance workloads among the Q nodes
storing copies of the streamlet 4102 by directing the MX node 461
(e.g., with a read grant) to a particular Q node that has, for
example, less current read and write grants as compared to other Q
nodes storing copies of the streamlet 4102.
[0089] A Q node storing a particular copy in a chain of copies of a
streamlet may fail, e.g., a computing process on the Q node storing
the particular copy may freeze. Other failure modes of a Q node are
possible. An MX node can detect a failed node (e.g., from
non-responsiveness of the failed node) and report the failed node
to a configuration manager in the messaging system 100 (e.g.,
configuration manager 214). A peer Q node can also detect a failed
Q node and report the failed node to the configuration manager. For
instance, an upstream Q node may detect a failed downstream Q node
when the downstream Q node is non-responsive, e.g., fails to
acknowledge a message storage request from the upstream Q node as
described earlier. It is noted that failure of a Q node storing a
copy of a particular streamlet of a particular channel stream does
not have to be for publish or subscribe operations of the
particular streamlet or of the particular channel stream. Failure
stemming from operations on another streamlet or another channel
stream can also alert a configuration manager about failure of a Q
node in the messaging system 100.
[0090] When a Q node storing a particular copy in a chain of copies
of a streamlet fails, a configuration manager in the messaging
system 100 can repair the chain by removing the failed node, or by
inserting a new node for a new copy into the chain, for example.
FIGS. 5C and 5D are data flow diagrams of example methods for
repairing a chain of copies of a streamlet in the messaging system
100. In FIG. 5C, for instance, after detecting that the Q node 504
fails, the configuration manager 214 can repair the chain of copies
by redirecting messages intended to be stored in the copy #3 of the
streamlet 4102 on the Q node 502 to the copy #4 of the streamlet
4102 on the Q node 506. In this example, a message (or a block of
messages) is first sent from the MX node 204 to the Q node 208 for
storage in the copy #1 of the streamlet 4102 (572). The message
then is forwarded to the Q node 502 for storage in the copy #2 of
the streamlet 4102 (574). The message is then forwarded to the Q
node 506 for storage in the copy #4 of the streamlet 4102 (576).
The Q node 506 can send an acknowledgement message to the
configuration manager 214 indicating that the message has been
stored successfully.
[0091] Here, a failed node can also be the node storing the head
copy or the tail copy of the chain of copies. For instance, if the
Q node 208 fails, the configuration manager 214 can instruct the MX
node 204 first to send the message to the Q node 502 for storage in
the copy #2 of the streamlet 4102. The message is then forwarded to
the next adjacent copy in the chain for storage, until the message
is stored in the tail copy.
[0092] If the Q node 506 fails, the configuration manager 214 can
repair the chain of copies of the streamlet 4102 such that the copy
#3 on the Q node 504 becomes the tail copy of the chain. A message
is first stored in the copy #1 on the Q node 208, then subsequently
stored in the copy #2 on the Q node 502, and the copy #3 on the Q
node 504. The Q node 504 then can send an acknowledgement message
to the configuration manager 214 indicating that the message has
been stored successfully.
[0093] In FIG. 5D, the configuration manager 214 replaces the
failed node Q node 504 by allocating a new Q node 508 to store a
copy #5 of the chain of copies of the streamlet 4102. In this
example, the configuration manager 214 instructs the MX node 204 to
send a message (from the publishers 406) to the Q node 208 for
storage in the copy #1 of the streamlet 4102 (582). The message is
then forwarded to the Q node 502 for storage in the copy #2 of the
streamlet 4102 (584). The message is then forwarded to the Q node
508 for storage in the copy #5 of the streamlet 4012 (586). The
message is then forwarded to the Q node 506 for storage in the copy
#4 of the streamlet 4102 (588). The Q node 506 can send an
acknowledgement message to the configuration manager 214 indicating
that the message has been stored successfully.
[0094] FIG. 6 is a data flow diagram 600 illustrating the
application of selective filtering, searching, transforming,
querying, aggregating and transforming of messages in real time to
manage the delivery of messages into and through each channel and
on to individual subscribers. Users operating applications on
client devices, such as, for example, smartphones, tablets, and
other internet-connected devices, act as subscribers (e.g.,
subscriber 480 in FIG. 4B, subscriber 602 in FIG. 6). The
applications may be, for example, consumers of the messages to
provide real-time information about news, transportation, sports,
weather, or other subjects that rely on published messages
attributed to one or more subjects and/or channels. Message
publishers 604 can be any internet-connected service that provides,
for example, status data, transactional data or other information
that is made available to the subscribers 602 on a subscription
basis. In some versions, the relationship between publishers and
channels is 1:1, that is there is one and only one publisher that
provides messages into that particular channel. In other instances,
the relationship may be many-to-one (more than one publisher
provides messages into a channel), one-to-many (a publisher's
messages are sent to more than one channel), or many-to-many (more
than one publisher provides messages to more than one channel).
Typically, when a subscriber subscribes to a channel, they receive
all messages and all message data published to the channel as soon
as it is published. The result, however, is that many subscribers
can receive more data (or data that requires further processing)
than is useful. The additional filtering or application of
functions against the data places undue processing requirements on
the subscriber application and can delay presentation of the data
in its preferred format.
[0095] A filter 606 can be created by providing suitable query
instructions at, for example, the time the subscriber 602
subscribes to the channel 608. The filter 606 that is specified can
be applied to all messages published to the channel 608 (e.g., one
message at a time), and can be evaluated before the subscriber 602
receives the messages (e.g., see Step 2 in FIG. 6). By allowing
subscribers 602 to create query instructions a priori, that is upon
subscribing to the channel 608 and before data is received into the
channel 608, the burden of filtering and processing messages moves
closer to the data source, and can be managed at the channel level.
As a result, the messages are pre-filtered and/or pre-processed
before they are forwarded to the subscriber 602. Again, the query
instructions need not be based on any a priori knowledge of the
form or substance of the incoming messages. The query instructions
can be used to pre-process data for applications such as, for
example, real-time monitoring services (for transportation,
healthcare, news, sports, weather, etc.) and dashboards (e.g.,
industrial monitoring applications, financial markets, etc.) to
filter data, summarize data and/or detect anomalies. One or more
filters 606 can be applied to each channel 608.
[0096] The query instructions can implement real-time searches and
queries, aggregate or summarize data, or transform data for use by
a subscriber application. In some embodiments, including those
implementing JSON formatted messages, the messages can be
generated, parsed and interpreted using the query instructions, and
the lack of a pre-defined schema (unlike conventional
RDBMS/SQL-based applications) means that the query instructions can
adapt to changing business needs without the need for schema or
application layer changes. This allows the query instructions to be
applied selectively at the message level within a channel, thus
filtering and/or aggregating messages within the channel. In some
instances, the queries may be applied at the publisher
level--meaning channels that receive messages from more than one
publisher may apply certain filters against messages from specific
publishers. The query instructions may be applied on a
going-forward basis, that is on only newly arriving messages,
and/or in some cases, the query instructions may be applied to
historical messages already residing in the channel queue.
[0097] The query instructions can be applied at either or both of
the ingress and egress side of the PubSub service. On the egress
side, the query instructions act as a per-connection filter against
the message channels, and allows each subscriber to manage their
own set of unique filters. On the ingress side, the query
instructions operate as a centralized, system-wide filter that is
applied to all published messages.
[0098] For purposes of illustration and not limitation, examples of
query instructions that may be applied during message ingress
include: [0099] A message may be distributed to multiple channels
or to a different channel (e.g., based on geo-location in the
message, or based on a hash function of some value in the message).
[0100] A message may be dropped due to spam filtering or DoS rules
(e.g., limiting the number of messages a publisher can send in a
given time period). [0101] An alert message may be sent to an admin
channel on some event arriving at any channel (e.g.,
cpu_temp>threshold).
[0102] For purposes of illustration and not limitation, examples of
query instructions that may be applied during message egress
include: [0103] Channels that contain events from various sensors
where the user is only interested in a subset of the data sources.
[0104] Simple aggregations, where a system reports real time
events, such as cpu usage, sensor temperatures, etc., and we would
like to receive some form of aggregation over a short time period,
irrespective of the number of devices reporting or the reporting
frequency, e.g., average(cpu_load), max(temperature),
count(number_of_users), count(number of messages) group by country.
[0105] Transforms, where a system reports real time events and
metadata is added to them from mostly static external tables, e.g.,
adding a city name based on IP address, converting an advertisement
ID to a marketing campaign ID or to a marketing partner ID. [0106]
Adding default values to event streams where such values do not
exist on certain devices. [0107] Advanced aggregations, where a
system reports real time events, and combines some mostly static
external tables data into the aggregation in real time, e.g.,
grouping advertisement clicks by partners and counting number of
events. [0108] Counting number of user events, grouping by a/b test
cell allocation.
[0109] In some embodiments, the query instructions may be used to
define an index or other suitable temporary data structure, which
may then be applied against the messages as they are received into
the channel to allow for the reuse of the data element(s) as
searchable elements. In such cases, a query frequency may be
maintained to describe the number of times (general, or in a given
period) that a particular data element is referred to or how that
element is used. If the frequency that the data element is used in
a query exceeds some threshold, the index may be stored for
subsequent use on incoming messages, whereas in other instances in
which the index is used only once (or infrequently) it may be
discarded. In some instances, the query instruction may be applied
to messages having arrived at the channel prior to the creation of
the index. Thus, the messages are not indexed according to the data
elements described in the query instructions but processed using
the query instructions regardless, whereas messages arriving after
the creation of the index may be filtered and processed using the
index. For queries or other subscriptions that span the time at
which the index may have been created, the results of applying the
query instructions to the messages as they are received and
processed with the index may be combined with results of applying
the query instructions to non-indexed messages received prior to
receipt of the query instructions.
[0110] For purposes of illustration and not limitation, one use
case for such a filtering application is a mapping application that
subscribes to public transportation data feeds, such as the
locations of all buses across a city. The published messages may
include, for example, geographic data describing the location,
status, bus agency, ID number, route number, and route name of the
buses. Absent pre-defined query instructions, the client
application would receive individual messages for all buses.
However, query instructions may be provided that filter out, for
example, inactive routes and buses and aggregate, for example, a
count of buses by agency. The subscriber application receives the
filtered bus data in real time and can create reports, charts and
other user-defined presentations of the data. When new data is
published to the channel, the reports can be updated in real time
based on a period parameter (described in more detail below).
[0111] The query instructions can be provided (e.g., at the time
the subscriber subscribes to the channel) in any suitable format or
syntax. For example, the following illustrates the structure of
several fields of a sample subscription request Protocol Data Unit
(PDU) with the PDU keys specific to adding a filter to a
subscription request:
TABLE-US-00002 { ''action'': ''subscribe'', "body": { ''channel'':
"ChannelName" ''filter'': "QueryInstructions" ''period'': [1-60,
OPTIONAL] } }
In the above subscription request PDU, the "channel" field can be a
value (e.g., string or other appropriate value or designation) for
the name of the channel to which the subscriber wants to subscribe.
The "filter" field can provide the query instructions or other
suitable filter commands, statements, or syntax that define the
type of key/values in the channel message to return to the
subscriber. The "period" parameter specifies the time period in,
for example, seconds, to retain messages before returning them to
the subscriber (e.g., an integer value from 1 to 60, with a default
of, for example, 1). The "period" parameter will be discussed in
more detail below. It is noted that a subscription request PDU can
include any other suitable fields, parameters, or values.
[0112] One example of a query instruction is a "select" filter,
which selects the most recent (or "top") value for all (e.g.,
"select.*") or selected (e.g., "select.name") data elements. In the
example below, the Filter column shows the filter value sent in the
query instructions as part of a subscription as the filter field.
The Message Data column lists the input of the channel message data
and the message data sent to the client as output. In this example,
the value for the "extra" key does not appear in the output, as the
"select" filter can return only the first level of results and does
not return any nested key values.
TABLE-US-00003 Filter Message Data SELECT * Input {"name": "art",
"eye": "blue"), {"name": "art", "age": 11}, {"age": 12, "height":
190} Output {"name": "art", "age": 12, "eye": "blue", "height":
190} SELECT Input top.* {"top": {"age": 12, "eyes": "blue"}},
{"top": {"name": "joy", "height": 168), "extra": 1}, {"top":
{"name": "art"}} Output {"name": "art", "age": 12, "eye": "blue",
"height": 168}
[0113] For aggregative functions, all messages can be combined that
satisfy the query instructions included in the GROUP BY clause. The
aggregated values can then be published as a single message to the
subscriber(s) at the end of the aggregation period. The number of
messages that are aggregated depends on, for example, the number of
messages received in the channel in the period value for the
filter. For instance, if the period parameter is set to 1, and 100
messages are received in one second, all 100 messages are
aggregated into a single message for transmission to the
subscsriber(s). As an example, a query instruction as shown below
includes a filter to aggregate position data for an object,
grouping it by obj_id, with a period of 1:
SELECT*WHERE(<expression with aggregate function>)GROUP BY
obj_id
In this example, all messages published in the previous second with
the same obj_id are grouped and sent as a batch to the
subscriber(s).
[0114] In some embodiments, a MERGE(*) function can be used to
change how aggregated message data is merged. The MERGE(*) function
can return a recursive union of incoming messages over a period of
time. The merge function may be used, for example, to track
location data for an object, and the subscriber is interested in
the most recent values for all key/value pairs contained in a set
of aggregated messages. The following statement shows an exemplary
syntax for the MERGE(*) function:
SELECT[expr][name,]MERGE(*)[*][AS name][FROM expr][WHERE
expr][HAVING expr]GROUP BY name
[0115] The following examples illustrate how the MERGE(*) function
may be applied within query instructions to various types of
channel messages. In the following examples, the Filter column
shows the filter value included in the query instructions as part
of a subscription request as the FILTER field. The Message Data
column lists the Input channel message data and the resulting
message data sent to the subscriber as Output. The filter returns
the most recent values of the keys identified in the input
messages, with the string MERGE identified as the column name in
the output message data. The first example below shows the MERGE(*)
function in a filter with a wildcard, for the message data is
returned using the keys from the input as column names in the
output.
TABLE-US-00004 Filter Message Data SELECT Input MERGE(*) {"name":
"art", "age": 10}, {"name": "art", "age": 11, "items": [0]} Output
{"MERGE": {"name": "art", "age": 11, "items": [0]}}
The next example illustrates the use of the MERGE(*) function in a
filter using a wildcard and the "AS" statement with a value of
MERGE. The output data includes MERGE as the column name.
TABLE-US-00005 Filter Message Data SELECT Input MERGE(*).* {
"name": "art", "age": 12, "items": [0], "skills": { "work":
["robots"] } }, { "name": "art", "age": 13, "items": ["car"],
"skills": { "home": ["cooking"] } } Output { "name": "art", "age":
13, "items": ["car"], "skills": { "work": ["robots"], "home":
["cooking"] } } SELECT Input MERGE(top.*) {"top": { }, "garbage":
0}, AS merge {"top": {"name": "art", "eyes": "blue"}}, {"top":
{"name": "joy", "height": 170}} Output {"merge": {"name": "joy",
"eyes": "blue", "height": 170}}
[0116] Generally, for aggregative functions and for filters that
only include a SELECT(expr) statement, only the latest value for
any JSON key in the message data from the last message received can
be stored and returned. Therefore, if the most recent message
received that satisfies the filter statement is missing a key value
identified in a previously processed message, that value is not
included in the aggregate, which could result in data loss.
However, filters that also include the MERGE(*) function can retain
the most recent value for all keys that appear in messages to an
unlimited JSON object depth. Accordingly, the most recent version
of all key values can be retained in the aggregate.
[0117] The MERGE(*) function can be used to ensure that associated
values for all keys that appear in any message during the
aggregation period also appear in the final aggregated message. For
example, a channel may track the physical location of an object in
three dimensions: x, y, and z. During an aggregation period of one
second, two messages are published to the channel, one having only
two parameters: OBJ{x:1, y:2, z:3} and OBJ{x:2, y:3}. In the second
message, the z value did not change and was not included in the
second message. Without the MERGE(*) function, the output result
would be OBJ{x:2, y:3}. Because the z value was not present in the
last message in the aggregation period, the z value was not
included in the final aggregate. However, with the MERGE(*)
function, the result is OBJ{x:2, y:3, z:3}.
[0118] The following table shows one set of rules that may be used
to aggregate data in messages, depending on the type of data. For
arrays, elements need not be merged, but instead JSON values can be
overwritten for the array in the aggregate with the last array
value received.
TABLE-US-00006 Type of Data to Aggregate Without With JSON Data
{msg1}, {msg2} MERGE(*) MERGE(*) Additional {a: 1, b: 2}, {c: 3}
{c: 3} {a: 1, b: 2, c: 3} key/value Dififerent {a: 2}, {a: "2"} {a:
"2"} {a: "2"} value datatype Missing {a: 2}, { } {a: 2} {a: 2}
key/value null value {a: 2}, {a: null} {a: null} {a: null}
Dififerent {a: {b: 1}}, {a: {c: 2}} {a: {c: 2}} {a: {b: 1, c: 2}}
key value Arrays {a: [1, 2]}, {a: [3, 4]} {a: [3, 4]} {a: [3,
4]}
[0119] The query instructions can be comprised of one or more
suitable filter commands, statements, functions, or syntax. For
purposes of illustration and not limitation, in addition to the
SELECT and MERGE functions, the query instructions can include
filter statements or functions, such as, for example, ABS(expr),
AVG(expr), COALESCE(a[, b . . . ]), CONCAT(a[, b . . . ]),
COUNT(expr), COUNT_DISTINCT(expr), IFNULL(expr1, expr2),
JSON(expr), MIN(expr[, expr1, . . . ]), MAX(expr[, expr1, . . . ]),
SUBSTR(expr, expr1[, expr2]), SUM(expr), MD5(expr), SHA1(expr),
FIRST_VALUE(expr) OVER (ORDER BY expr1), and/or LAST_VALUE(expr)
OVER (ORDER BY expr1), where "expr" can be any suitable expression
that is capable of being processed by a filter statement or
function, such as, for example, a SQL or SQL-like expression. Other
suitable filter commands, statements, functions, or syntax are
possible for the query instructions.
[0120] According to the present invention, non-filtered queries can
translate to an immediate copy of the message to the subscriber,
without any JSON or other like processing. Queries that include a
SELECT filter command (without aggregation) can translate into an
immediate filter. In instances in which the messages are formatted
using JSON, each message may be individually parsed and any WHERE
clause may be executed directly on the individual message as it
arrives, without the need for creating indices or other temporary
data structures. If the messages pass the WHERE clause filter, the
SELECT clause results in a filtered message that can be converted
back to its original format or structure (e.g., JSON) and sent to
the subscriber.
[0121] Aggregative functions, such as, for example, COUNT( ), SUM(
), AVG( ), and the like, can translate into an immediate
aggregator. In instances in which the messages are formatted using
JSON, each message may be individually parsed and any WHERE clause
may be executed directly on the individual message as it arrives,
without the need for creating indices or other temporary data
structures. If a WHERE clause is evaluated, messages passing such
criteria are aggregated (e.g., aggregates in the SELECT clause are
executed, thereby accumulating COUNT, SUM, AVG, and so forth) using
the previous accumulated value and the value from the individual
message. Once per aggregation period (e.g., every 1 second), the
aggregates are computed (e.g., AVG=SUM/COUNT), and the SELECT
clause outputs the aggregated message, which can be converted to
its original format or structure (e.g., JSON) and sent to the
subscriber.
[0122] More complex aggregative functions, such as, for example,
GROUP BY, JOIN, HAVING, and the like, can be translated into a hash
table aggregator. Unlike SELECT or other like functions that can
use a constant memory, linearly expanding memory requirements can
be dependent upon the results of the GROUP BY clause. At most,
grouping by a unique value (e.g., SSN, etc.) can result in a group
for each individual message, but in most cases grouping by a common
data element (e.g., user_id or other repeating value) can result in
far fewer groups. In practice, each message is parsed (from its
JSON format, for example). The WHERE clause can be executed
directly on the individual message as it arrives, without creating
indices or other temporary structures. If the WHERE clause is
satisfied, the GROUP BY expressions can be computed directly and
used to build a hash key for the group. The aggregative functions
in the SELECT clause can be executed, accumulating COUNT, SUM, AVG,
or other functions using the previous accumulated value specific
for the hash key (group) and the value from the individual message.
Once per aggregation period (e.g., every 1 second), the aggregates
are computed (e.g., AVG=SUM/COUNT) for each hash key (group), and
the SELECT clause can output the aggregated message for each hash
key to be converted back to its original format or structure (e.g.,
JSON) and sent to the subscriber (e.g., one message per hash key
(group)).
[0123] In embodiments in which the aggregation period is limited
(e.g., 1 second-60 seconds) and the network card or other
hardware/throughput speeds may be limited (e.g., 10/gbps), the
overall maximal memory consumption can be calculated as time*speed
(e.g., 1 GB per second, or 60 GB per minute). Hence, the upper
bound is independent of the number of subscribers. In certain
implementations, each message only need be parsed once (e.g., if
multiple filters are set by multiple clients) and only if needed
based on the query instructions, as an empty filter does not
require parsing the message.
[0124] Referring to FIG. 7A, subscriptions can include a "period"
parameter, generally defined in, for example, seconds and in some
embodiments can range from 1 to 60 seconds, although other time
increments and time ranges are possible. The period parameter(s)
can be purely sequential (e.g., ordinal) and/or time-based (e.g.,
temporal) and included in the self-described data and therefore
available for querying, aggregation, and the like. For example,
FIG. 7A illustrates the filter process according to the present
invention for the first three seconds with a period of 1 second. In
the present example, the subscription starts at t=0. The filter
created from the query instructions is applied against all messages
received during each 1-second period (e.g., one message at a time).
The results for each period are then batched and forwarded to the
subscriber. Depending on the query instructions used, the messages
can be aggregated using the aggregation functions discussed
previously before the message data is sent to the subscriber.
[0125] In some cases, the process defaults to sending only new,
incoming messages that meet the query instructions on to the
subscriber. However, a subscriber can subscribe with history and
use a filter, such that the first message or messages sent to the
subscriber can be the historical messages with the filter applied.
Using the period of max_age and/or a "next" parameter provides
additional functionality that allows for retrieval and filtering of
historical messages.
[0126] More particularly, a max_age parameter included with the
query instructions can facilitate the retrieval of historical
messages that meet this parameter. FIG. 7B illustrates an example
of a max_age parameter of 2 seconds (with a period of 1 second)
that is provided with the query instructions. The filter created
from the query instructions is applied to the historical messages
from the channel that arrived from t-2 through t=0 (t=0 being the
time the subscription starts), and to the messages that arrived in
the first period (from t=0 to t+1). These messages can be sent in a
single batch to the subscriber (as Group 1). The filter is applied
to each message in each subsequent period (e.g., from t+1 to t+2 as
Group 2) to batch all messages that meet the query instructions
within that period. Each batch is then forwarded on to the
subscriber.
[0127] When a subscriber subscribes with a "next" parameter to a
channel with a filter, the filter can be applied to all messages
from the next value up to the current message stream position for
the channel, and the results can be sent to the subscriber in, for
example, a single batch. For example, as illustrated in FIG. 7C, a
next parameter is included with the query instructions (with a
period of 1 second). The next parameter instructs the process to
apply the filter created from the query instructions to each
message from the "next position" up through the current stream
position (e.g., up to t=0) and to the messages that arrived in the
first period (from t=0 to t+1). These messages can be sent in a
single batch to the subscriber (as Group 1). The filter is applied
to each message in each subsequent period (e.g., from t+1 to t+2 as
Group 2) to batch all messages that meet the query instructions
within that period. Each batch is then forwarded on the
subscriber.
[0128] When a subscriber subscribes with a next parameter, chooses
to receive historical messages on a channel, and includes a filter
in the subscription, the subscriber can be updated to the current
message stream position in multiple batches. FIG. 7D illustrates an
example of a max_age parameter of 2 seconds (with a period of 1
second) and a next parameter that can be combined into one set of
query instructions. The filter created from the query instructions
is applied to the historical messages from the channel that arrived
from the end of the history to the "next" value of the subscription
(i.e., from 2 seconds before the next value up to the next value),
to the messages from the next value to the current stream position
(e.g., up to t=0), and to the messages that arrived in the first
period (from t=0 to t+1). These messages can be sent in a single
batch to the subscriber (as Group 1). The filter is applied to each
message in each subsequent period (e.g., from t+1 to t+2 as Group
2) to batch all messages that meet the query instructions within
that period. Each batch is then forwarded on the subscriber.
Consequently, historical messages can be combined with messages
that start at a particular period indicator and batched for
transmission to the subscriber.
[0129] The query instructions can define how one or more filters
can be applied to the incoming messages in any suitable manner. For
example, the resulting filter(s) can be applied to any or all
messages arriving in each period, to any or all messages arriving
across multiple periods, to any or all messages arriving in select
periods, or to any or all messages arriving on a continuous or
substantially continuous basis (i.e., without the use of a period
parameter such that messages are not retained before returning them
to the subscriber). Such filtered messages can be batched in any
suitable manner or sent individually (e.g., one message at a time)
to subscribers. In particular, the filtered messages can be sent to
the subscriber in any suitable format or syntax. For example, the
following illustrates the structure of several fields of a sample
channel PDU that contains the message results from a filter
request:
TABLE-US-00007 { ''action'': ''channel/data'', "body": {
''channel'': ChannelName ''next'': ChannelStreamPosition
''messages'': [ChannelData]+ // Can be one or more messages } }
In the above channel PDU, the "channel" field can be a value (e.g.,
string or other appropriate value or designation) of the channel
name to which the subscriber has subscribed. The "next" field can
provide the channel stream position of the batch of messages
returned in the channel PDU. The "messages" field provides the
channel data of the messages resulting from application of the
specified filter. One or more messages can be returned in the
"messages" field in such a channel PDU. It is noted that a channel
PDU can include any other suitable fields, parameters, values, or
data.
[0130] Turning now to FIG. 8, an example diagram of a map 800
(e.g., radio map) is illustrated. As depicted, the map 800 may
include a map of an environment 801. The environment 801 may
include, for example, an indoor environment (e.g., inside of
residential, commercial, or industrial buildings), an outdoor
environment (e.g., a public or private campus, a neighborhood, a
town, a city, a country, a geographical region, and so forth), or a
combination of an indoor and outdoor environments (e.g., a building
and adjacent parking and recreational areas). As illustrated, the
map 800 may include a number of locations 802 (e.g., rooms within a
building, or specific areas or regions of a campus) by which a
number nodes 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, and
824 may either be located or move within and about. In certain
embodiments, each of the nodes 804, 806, 808, 810, 812, 814, 816,
818, 820, 822, and 824 may represent any of various client
electronic devices (e.g., mobile electronic devices [e.g., mobile
phones, tablet computers, laptop computers, cameras], in-home
electronic devices [e.g., video game consoles, smoke detectors,
thermostats, gateway devices, desktop computers, projectors],
wearable electronic devices [e.g., smartwatches, wristbands,
pedometers, electronic eyewear, electronic headwear], and so forth)
that may be associated with users stationary or moving within or
about the environment 801.
[0131] In one embodiment, each of the nodes 804, 806, 808, 810,
812, 814, 816, 818, 820, 822, and 824 may be connected to a
wireless local area network (WLAN) (e.g., Wi-Fi.TM., UWB, White-Fi,
and so forth), a cellular network (e.g., 4G, LTE.TM., 5G,
LTE-LAA.TM., LTE-U.TM., and so forth), personal area network (PAN)
(e.g., 6LowWPAN.TM., RuBee.TM., Z-Wave, ZigBee.TM., WANET, and so
forth), or other similar wireless network within or about the
environment 801. As will be further appreciated with respect to
FIG. 9, the nodes 804, 806, 808, 810, 812, 814, 816, 818, 820, 822,
and 824 may each be in communication with one or more server
electronic devices to send and receive messages regarding an
approximate geolocation of the nodes 804, 806, 808, 810, 812, 814,
816, 818, 820, 822, and 824 with respect to the environment
801.
[0132] In certain embodiments, as further illustrated by FIG. 8,
some of the nodes 808, 812, 814, 816, 818, and 820 may include
adjacent arrow illustrations while others may not. Specifically, in
accordance with the present embodiments, the nodes 808, 812, 814,
816, 818, and 820 (e.g., those including no adjacent arrow
illustrations) may be stationary within or between pathways of the
environment 801. Similarly, nodes 804, 806, 810, 822, and 824
(e.g., those including the adjacent arrow illustrations) may be
moving within or between pathways of the environment 801. It should
be appreciated that each of the nodes 804, 806, 808, 810, 812, 814,
816, 818, 820, 822, and 824 may, at different times, for example,
be stationary or mobile within or about the environment 801. In
accordance with the present embodiments, a server electronic device
in communication with the nodes 804, 806, 808, 810, 812, 814, 816,
818, 820, 822, and 824 may determine and track the geolocation of
the nodes 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, and 824
within or about the environment 801.
[0133] For example, as will be further appreciated with respect to
FIGS. 9-12, the present embodiments may include a system
architecture for locating and tracking electronic devices in indoor
and outdoor environments. The messaging system may support the
PubSub communication pattern and may allow publishers and
subscribers to publish and receive live messages. Users of certain
client electronic devices may want to determine and track other
electronic devices, and thus may be both publishers and subscribers
of the messaging system. Electronic devices may publish messages to
indicate the approximate geolocations of the electronic devices.
Users may view the messages corresponding to live approximate
geolocations of the electronic devices as the users of the
electronic devices move within or about indoor or outdoor
environments. For example, as will be described below, the present
embodiments may be directed to a schema for collaborative
geo-positioning of multiple electronic devices that may improve
triangulation accuracy and precision of locating each electronic
device of a number of electronic devices by calculating and
recording information that may be used to improve accuracy and
precision of future approximate geolocations. The present
techniques also minimize the amount of information stored on
servers that could be used to track and locate devices in indoor
and outdoor environments or even worldwide in some examples. In
this way, the present embodiments may provide techniques to
efficiently determine and track the approximate geolocation of
electronic devices within or about indoor and outdoor environments,
or otherwise in any of various environments in which large-scale
satellite systems may be inaccurate, imprecise, or otherwise
unavailable.
[0134] With the foregoing in mind, FIG. 9 is a diagram of an
example system architecture 900 that may be used to track and
locate client electronic devices stationary or moving within or
about, for example, the environment 801. In accordance with at
least some of the present embodiments, the system architecture 900
may include a geo-positioning network (GPSNET), and, in some
embodiments, may correspond to the system 100 discussed above with
respect to FIGS. 1A and 1B. For example, as depicted, the system
architecture 900 may include a server electronic device 901 (or
numerous server electronic devices 901) and a number of client
electronic devices 916, 918, 920, 922, and 924 (e.g., corresponding
to one or more of the nodes 804, 806, 808, 810, 812, 814, 816, 818,
820, 822, and 824 discussed in FIG. 8) that may be in communication
with the server electronic device 901. The server electronic device
901 may support the PubSub communication pattern, as described
earlier in reference to FIGS. 1A through 5D. In some embodiments,
the server electronic device 901 may be referred to as a PubSub
system or a PubSub messaging system. As illustrated, the server
electronic device 901 may include, for example, a channel 902, a
channel 904, a channel 906, a channel 908, a channel 910, a channel
912, and a channel 914. The messages published to channels 902,
904, 906, 908, 910, 912, and 914 (e.g., channel streams) may be
divided into streamlets, which may be stored within Q nodes or one
more databases of the server electronic device 901, as generally
described, for example, earlier in reference to FIGS. 1A through
5D. C nodes of the messaging system may be used to offload data
transfers from one or more Q nodes (e.g., to cache some of the
streamlets stored in the Q nodes).
[0135] In certain embodiments, the client electronic devices 916,
918, 920, 922, and 924 may establish respective persistent
connections (e.g., TCP communications or other similar
communications channels) to one or more MX nodes. The one or more
MX nodes may serve as termination points for these connections, as
described earlier in reference to FIGS. 1A through 5D. As further
illustrated, each of the client electronic devices 916, 918, 920,
922, and 924 may include one or more respective application
components 926, 928, 930, 932, and 934, which may, for example,
allow users to subscribe to and publish to the channels 902, 904,
906, 908, 910, 912, and 914 of the server electronic device 901.
For example, the server electronic device 901 may authenticate
users and determine whether users are allowed to publish to certain
channels 902, 904, 906, 908, 910, 912, and 914, in some
embodiments.
[0136] Turning now to FIG. 10, which illustrates a flow diagram of
a method 1000 of determining and tracking the approximate
geolocation of one or more client electronic devices (e.g., client
electronic devices 916, 918, 920, 922, and 924 discussed with
respect to FIG. 9) in accordance with the present embodiments. In
certain embodiments, the method 1000 may be performed by processing
logic that may include hardware such as one or more computer
processing devices, software (e.g., instructions running/executing
on a computer processing device), firmware (e.g., microcode), or a
combination thereof, such as the server electronic device 901
discussed above with respect to FIG. 9. For the purpose of
illustration and breadth, henceforth, the method 1000 of FIG. 10,
the method 1100 of FIG. 11, and the method 1200 of FIG. 12 will be
described in conjunction with various examples and in reference to
FIGS. 8 and 9 to illuminate and delineate the present
techniques.
[0137] The method 1000 of FIG. 10 may begin with a computer
processing device of the server electronic device 901 receiving
desired operational parameters from one or more of a plurality of
client electronic devices (Step 1002). For example, referring again
to FIG. 9, one or more of the client electronic devices 916, 918,
920, 922, and 924 may transmit a request to the server electronic
device 901 in which the request may include a message to the server
electronic device 901 updating, for example, user preferences. In
some embodiments, the user preferences may be updated by the
application components 926, 928, 930, 932, and 934 running on the
client electronic devices 916, 918, 920, 922, and 924, and may
include, for example, desired operational parameters such as
frequency, accuracy, power consumption, processing capacity,
bandwidth, hardware usage, and similar operational parameters that
may be specified by a user of the client electronic devices 916,
918, 920, 922, and 924.
[0138] For example, frequency may include a measurement of time
specifying the interval at which the server electronic device 901
may endeavor to produce a location estimate. Similarly, accuracy
may include a measurement of distance d and a measurement of
probability p, specifying that the server electronic device 901 may
endeavor to return delivery confidences such that there is a
p-percent or greater probability that the client electronic devices
916, 918, 920, 922, and 924 true location is within d distance of
the delivered estimate. Power consumption, for example, may include
two unit-less values indicating, for example, a user's willingness
to sacrifice desired frequency and accuracy in exchange for lower
power consumption (e.g., battery usage). Processing capacity may
include two unit-less numbers indicating, for example, a user's
willingness to sacrifice desired frequency and accuracy in exchange
for lower processing robustness. Lastly, other hardware usage may
include two unit-less values per additional hardware resource
indicating, for example, the user's willingness to sacrifice
desired frequency and accuracy in exchange for instructions that
use fewer hardware resources of the server electronic device
901.
[0139] The method 1000 may continue with one or more computer
processing devices of the server electronic device determining
measurement instructions based on the desired operational
parameters (Step 1004). For example, the server electronic device
901 may generate and provide instructions to one or more of the
client electronic devices 916, 918, 920, 922, and 924 to ping one
or more other client electronic devices 916, 918, 920, 922, and
924, measure data (e.g., distance data between client electronic
devices), and report the measured and captured data back to the
server electronic device 901. Specifically, in certain embodiments,
the server electronic device 901 may estimate the approximate
geolocation of one or more of the client electronic devices 916,
918, 920, 922, and 924 by sending instructions to at least some of
the client electronic devices 916, 918, 920, 922, and 924 to ping,
measure, and report their measurements (e.g., distance data,
velocity data, acceleration data, accelerometer data, magnetometer
data, gyroscope data, GPS data, and so forth). In some embodiments,
the measurement instructions provided by the server electronic
device 901 may be specific to each of the client electronic devices
916, 918, 920, 922, and 924 and may be based on, for example, the
desired operational parameters (e.g., frequency, accuracy, power
consumption, processing capacity, bandwidth, hardware usage, and so
forth) received in the request.
[0140] The method 1000 may continue with one or more computer
processing devices of the server electronic device transmitting the
measurement instructions to at least a subset of the client
electronic devices (Step 1006). For example, the server electronic
device 901 may send each or a subset of the client electronic
devices 916, 918, 920, 922, and 924 an instruction message, which
may include, for example, a decision-tree indicating which
instruction steps should be executed and at which time.
Specifically, each measureable outcome of an execution may then
determine which instruction step or execution to perform next.
[0141] In certain embodiments, the instructions provided by the
server electronic device 901 to the client electronic devices 916,
918, 920, 922, and 924 may be structured such that when one of the
client electronic devices 916, 918, 920, 922, and 924 is instructed
to measure, then the server electronic device 901 has already
calculated that a probability threshold (e.g., high enough
probability) has been achieved, thus indicating that the
measurement is likely to be successful in measuring, for example, a
physical distance, a signal intensity, a pattern, an image,
distance data, RF signal intensity data, latency data, response
time data, image data, audio data, voice data, biometric data,
video data, temperature data, humidity data, atmospheric data,
light data, magnetic data, social media data, or other measurement
data that may improve certainty in future geolocation calculations
to be performed by the server electronic device 901.
[0142] In some embodiments, for example, the server electronic
device 901 may determine whether the probability threshold (e.g.,
minimum acceptable probability or above 50% probability, above 60%
probability, above 70% probability, above 80% probability, above
90% probability) has been achieved based on whether another client
electronic device 916, 918, 920, 922, and 924 has been instructed
to ping, or whether that particular client electronic device has
met a probability threshold (e.g., minimum acceptable probability
or above 50% probability, above 60% probability, above 70%
probability, above 80% probability, above 90% probability) with
respect to being nearby. The server electronic device 901 may also
determine whether the probability threshold (e.g., minimum
acceptable probability or above 50% probability, above 60%
probability, above 70% probability, above 80% probability, above
90% probability) has been achieved based on whether the particular
client electronic device has been instructed to set hardware
parameters to values that have a high probability (e.g.,
probability is high enough) with respect to being compatible with
the measurement parameters captured, for example, by the initial
client electronic device.
[0143] In other embodiments, for example, the server electronic
device 901 may determine whether the probability threshold (e.g.,
minimum acceptable probability or above 50% probability, above 60%,
probability, above 70% probability, above 80% probability, above
90% probability) has been achieved based on whether there is an
object with, for example, a quasi-permanent location and that is
transmitting a signal, an image, audio data, voice data, biometric
data, video data, temperature data, humidity data, light data,
magnetic data, social media data, or other similar identifiable
measurement data that can be detected by any of the client
electronic devices 916, 918, 920, 922, and 924 or the server
electronic device 901 with a minimum acceptable probability. In
certain embodiments, as previously noted, the client electronic
devices 916, 918, 920, 922, and 924 may process the outcome of each
instructed execution and refer again to the decision-tree of the
instructions to determine a manner in which to proceed. For
example, in one embodiment, a pair of the client electronic devices
916, 918, 920, 922, and 924 may be instructed to ping and measure
at the same time using a set of possible parameters, and once the
pair successfully transfers a message, the pair may be instructed
to terminate and report.
[0144] In certain embodiments, the terminal action of each
instruction decision-tree, regardless of any outcomes, is either
the command to "wait" for further instruction or the command to
report back to the server electronic device 901. If the client
electronic device 916, 918, 920, 922, and 924 is commanded to
report, then the information utilized to complete this action
(e.g., which measurements to report and what formatting to use) may
be included in that command in the instructions. It should be
appreciated that the measurement instruction messages provided to
the client electronic devices 916, 918, 920, 922, and 924 by the
server electronic device 901 may include any number of instructions
or sets of instructions. For example, the instructions may be any
number of actions (e.g., measure, ping, alert, report, store, pause
processing messages for a period of time, and so forth) to be
performed by one or more of the client electronic devices 916, 918,
920, 922, and 924.
[0145] In certain embodiments, identification data may also be sent
within each type of message. For example, identification data may
be included in the initial request from the client electronic
devices 916, 918, 920, 922, and 924 to the server electronic device
901, as well as in the ping message sent from, for example, one or
more of the client electronic devices 916, 918, 920, 922, and 924
to one or more other client electronic devices 916, 918, 920, 922,
and 924. In some embodiments, because each of the client electronic
devices 916, 918, 920, 922, and 924 that are near each other in
both space and time may report to the same server electronic device
901, each client electronic devices 916, 918, 920, 922, and 924 may
choose to divulge their respective data in limited form: not at all
times, or not to all parties, or not in complete form, or not in
continuous form. An example of incomplete divulgence includes the
use of a hashed identification number in place of its full
unencrypted literal. An example of an non-continuous form of
divulgence includes changing a client's identification number to a
new identification number unlinked to the previous, for
example.
[0146] The method 1000 may continue with the one or more computer
processing devices of the server electronic device receiving
measurement data from at least the subset of the plurality of
client electronic devices (Step 1008). In certain embodiments, the
server electronic device 901 may utilize these measurements to
triangulate the client electronic devices 916, 918, 920, 922, and
924 (or a subset thereof) as part of the present techniques to
determine the approximate geolocations of the client electronic
devices 916, 918, 920, 922, and 924 within or about the environment
801. For example, the server electronic device 901 may identify one
or more particular client electronic devices 916, 918, 920, 922,
and 924 whose current need for accuracy is higher than the current
estimated ability of the server electronic device 901 to provide
such an accurate estimate.
[0147] The server electronic device 901 may then utilize, for
example, a database to identify a list of client electronic devices
916, 918, 920, 922, and 924 and those objects (e.g., permanently
positioned objects or quasi-permanently positioned objects within
or about the environment 801) that are nearby with a minimum
acceptable probability (e.g., above a certain percentage). For
example, one or more databases of the server electronic device 901
may be utilized to codify the user behavior that is likely to be
performed by which client electronic devices 916, 918, 920, 922,
and 924 at which times for each possible location 802, for example.
In some embodiments, this information may be stored efficiently by
geographically sectioning the environment 801 and accumulating
summary information about each section and/or each location 802.
Specifically, the server electronic device 901 may utilize the
longitude, latitude, and altitude axes to section the captured and
stored information and generate, for example, a collectively
exhaustive and mutually exclusive (CEME) sectioning system.
[0148] In certain embodiments, the server electronic device 901 may
generate the CEME sectioning system by forming a CEME set of
latitude intervals. For example, in one embodiment, letting [0-1]
be the set of all latitudes between 0.0 and 1.0 degrees, the server
electronic server device 901 may generate, for example, intervals
[-90,-89], [-89,-88], . . . [-1,0], [0-1], [1-2], [2-3], . . .
[88-89], and [89-90] (e.g., up to approximately 180 intervals) as a
valid CEME set. Specifically, the server electronic device 901 may
form a CEME set of latitudes and form a CEME set of altitudes by,
for example, executing a Cartesian crossing of the CEME set of
latitudes and the CEME set of altitudes to form a 3-dimensional
(3-D) CEME sectioning of space of the environment 801, for example.
For example, in one embodiment, [0,-1], [0-1], and [0,1] may
include the cube-like area (which may be referred to herein as a
"cube") of space on latitudes between 0.0 and 1.0 degrees, on
longitudes between 0.0 and 1.0 degrees, and on altitudes between
0.0 to 1.0 meters above a determinable altitude level. For each
"cube," the server electronic device 901 may create another CEME
sectioning of 3-D space, further dividing the "cube" into smaller
"cubes," for example. In some embodiments, the server electronic
device 901 may repeat this process, for example, to increase
resolution over time. The server electronic device 901 may also
utilize the CEME sectioning as an index to identify which databases
of the server electronic device 901 are available and for which
regions are the databases available. In some embodiments, for
example when a database is added to the server electronic device
901, the added database may find all "cubes" of all sizes for which
it has information.
[0149] In certain embodiments, each database of the server
electronic device 901 may then store, for example in some sortable
order, a list of defined 3-D enclosures and information about the
enclosures. The enclosures might include, for example, the location
of certain obstructions or constructions (e.g., walls, islands,
columns, pillars, and so forth) or other quasi-permanent objects
that may at least partially skew or otherwise prevent measurement
data. The information stored by the server electronic device 901
may include, for example, which types of signals the objects
obstruct, the degree of obstruction (e.g., level of obstruction),
the location of "user hotspots" or other frequently visited spots
by users, the time-of-day and day-of-week the "user hotspots" are
frequented, the probability of users being at the "user hotspots"
at certain times, the probability of users being at the "user
hotspots" at certain times given the user, for example, was within
a certain radius at another time. The information stored by the
server electronic device 901 may also include, for example, the
delay of hardware signaling devices, formulas for calculating
estimated distance for each make and model of client electronic
devices 916, 918, 920, 922, and 924, the location of, for example,
pedestrian, automobile, train, and other similar pathways of the
environment 801, a list of enclosed areas of the environment 801,
and the distribution of observed speeds of client electronic
devices 916, 918, 920, 922, and 924 moving through certain areas.
The information stored by the server electronic device 901 may also
include, for example, functions indicating how that probability
distribution varies over time-of-day and day-of-week, functions
indicating how that distribution varies according to the make and
model of the client electronic devices 916, 918, 920, 922, and 924,
and so on and so forth.
[0150] In some embodiments, the server electronic device 901 may
calculate and store certain predictions with respect to users of
the client electronic devices 916, 918, 920, 922, and 924. For
example, in one embodiment, some prediction values may depend on,
for example, the current weather forecast or other similar data.
Such data, for example, may be utilized by the server electronic
device 901 to learn some behavior of the user and make one or more
predictions regarding, for example, the demographics and possible
locations of the user. In another embodiment, the user may
voluntarily input some additional personal or observed information
utilizing, for example, one or more applications running on the
client electronic devices 916, 918, 920, 922, and 924 such as the
application components 926, 928, 930, 932, and 934 previously
discussed. Such data may be received and stored by the server
electronic device 901 and may be utilized, in some embodiments, in
determining present or future measurement instructions to provide
to the client electronic devices 916, 918, 920, 922, and 924.
[0151] In some embodiments, as part of a delivery message, the
server electronic device 901 may also calculate the maximum
probable geographic location of the message source and destination.
Thus, for a message m, the server electronic device 901 may
calculate the most likely points x and y that the message m may be
sent and received. The location estimations x and y may be
constantly updated by the server electronic device 901. After some
amount of time after each message m is reported, the server
electronic device 901 may generate an estimate for the approximate
geolocation of that message's endpoint locations x and y. That
message m may include a hardware type, and may thus include an
implied distance d, which as will be further appreciated below with
respect to Step 1010 of FIG. 10, may be utilized by the server
electronic device 901 to infer distance (e.g., distance with
respect to the client electronic devices 916, 918, 920, 922, and
924) as part of the geolocation estimation calculation.
[0152] In certain embodiments, the server electronic device 901 may
also utilize a CEME set of intervals in location, velocity, and
other suitable or desirable information to store a distribution of
observed velocities. For example, a distribution may be based on
device ID or certain other distributions may be based on location
(e.g., "cubes" and enclosures). Angular velocity and acceleration
may be estimated, for example, for every three consecutive
geolocation estimates. The server electronic device 901 may weigh
recent information as more indicative of present outcomes by
utilizing, for example, one or more supervised learning and
cross-validation tuning techniques. In this way, the server
electronic device 901 may predict behavior and possible measurement
or signal outcomes. The server electronic device 901 may also
generate and provide different levels of confidence for the
probable estimations, as will be appreciated in greater detail with
respect to Step 1012 of FIG. 10.
[0153] For example, in some embodiments, the server electronic
device 901 may initially observe that a user ID associated with a
particular one of the client electronic devices 916, 918, 920, 922,
and 924 spends time evenly between two locations. However, once
unsupervised analysis is performed, the server electronic device
901 may determine that the distribution of locations varies
significantly between, for example, before noon (e.g., morning) and
afternoon (e.g., midday to late evening) each day. Thus, the server
electronic device 901 may henceforth also store the morning and
afternoon distributions for that particular user ID and client
electronic device, for example. Furthermore, as contiguous "cubes"
are identified as having similar properties (for one or more data
types), the server electronic device may define a new enclosure.
For example, large pathways such as large stretches of major
highways may be grouped into enclosures to represent, for example,
the distribution of traffic speed observed in each lane and at what
time of day and day-of-week. In such a case, the server electronic
device 901 may utilize unsupervised learning or clustering to
determine which contiguous "cubes" to utilize to make a new
enclosure. Specifically, in accordance with the present techniques,
the server electronic device 901 may accumulate behavioral maps
(e.g., user specific patterns) such as road maps, pedestrian
walkways, building maps, air density maps, signal interference
maps, and the like.
[0154] For example, in the example in which a company adds a new
building to its campus (e.g., environment 801) and the client
electronic devices 916, 918, 920, 922, and 924 send requests while
corresponding users walk throughout the new campus building, the
server electronic device 901 may incrementally update its
determination about each square foot of the area to reflect (e.g.,
based on fuzzy logic) a sense of the location of, for example, the
roads, walls, furniture areas, islands, columns, frequent
walk-paths, metal-plates, the location and basic shape of highly
unique images, and favorite work spots, walking speeds of the
company employees, frequent visitors, and so forth with respect to
the new campus building. The server electronic device 901 may then
utilize this information to predict the probable location and
velocity of current visitors and the probable distance of a
measurement reading based upon the historical behavior and signal
interference of previous observations, for example. As another
example, if users of the client electronic devices 916, 918, 920,
922, and 924 are walking past the front door of the new campus
building and, for example, the client devices include a magnetic
imaging measurement device, the server electronic device 901 may
detect a strong signal to the users' East, for example, over time.
The server electronic device 901 may utilize that additional
real-time information to further triangulate the approximate
geolocation of a client electronic device 916, 918, 920, 922, and
924 currently requesting a geolocation estimate, for example.
[0155] In certain embodiments, when one or more of the client
electronic devices 916, 918, 920, 922, and 924 reports its
findings, the server electronic device 901 may utilize the
information to validate or correct the previous expectations or
assumptions that formed the foundation of prior decisions (e.g.,
determine with certainty whether the server electronic device 901
now have enough information to meet the user preferences, whether
the client electronic devices and objects that the server
electronic device 901 calculated were nearby with high probability
are indeed nearby, whether additional client electronic devices and
objects have moved or remain stationary, whether additional user
preferences have been received by the server electronic device 901,
etc.).
[0156] The method 1000 may continue with the one or more computer
processing devices of the server electronic device generating
geolocation estimation data for the one or more client electronic
devices (Step 1010). For example, in certain embodiments, the
server electronic device 901 may estimate the approximate
geolocation of the client electronic devices 916, 918, 920, 922,
and 924 by utilizing, for example, supervised learning on the
probable location of a device ID based upon its historical
distribution, the probable distances between the client electronic
devices 916, 918, 920, 922, and 924, based on the measurement data,
the probable distance between each of the client electronic devices
916, 918, 920, 922, and 924 and known obstructions (e.g., walls,
magnetic fields, object images, and so forth), and the probable
velocities and accelerations of the client electronic devices 916,
918, 920, 922, and 924 based upon, for example, the historical
distribution of the device ID and the location historical
distribution of the environment 801.
[0157] In certain embodiments, for each delivery message, the
server electronic device 901 may determine an estimated location,
x=(lat, lon, alt), a radius=r, and a probability of one or more of
the client electronic devices 916, 918, 920, 922, and 924 being
within that distance of the estimate=p. For each client electronic
device 916, 918, 920, 922, and 924 respective device ID i, and for
each "cube" of environment 801 space, the database of the server
electronic device 901 may keep track of a cumulative score by, for
example, adding "points" at each iteration to a running tally. In
one embodiment, for some monotonically increasing functions f and
g, whenever the server electronic device 901 sends a delivery
message with (x, r, p), then
points ( y ) = f ( p , r - 1 ) g ( distance ( x , y ) ) ,
##EQU00001##
with distance (x, y) defined as the average Euclidean distance
between x and the set of points that constitute "cube" y. After
each delivery message, the server electronic device 901 may
cumulate the points at each "cube" by computing, for example,
cumulative points(y)=points(y). Thus, at any moment in time, for
any CEME set of "cubes" C, the server electronic device 901 may
estimate the prior probability that a particular client electronic
device 916, 918, 920, 922, and 924 is located in any "cube" y (that
belongs to C) as:
prior ( i , y ) = cumulative points ( y ) z .di-elect cons. C
cumulative points ( z ) ##EQU00002##
[0158] In another embodiment, the server electronic device 901 may
estimate the conditional probability that a particular client
electronic device 916, 918, 920, 922, and 924 is located in "cube"
y (given that it is located in a larger "cube" Y), where there
exists a set of CEME "cubes" C, such that Y=Union(C), and wherein y
enclosed fully by Y:
conditional ( i , y Y ) = cumulative points ( y ) z .di-elect cons.
CEME ( Y ) cumulative points ( z ) ##EQU00003##
[0159] In certain embodiments, to ensure that these probabilities
match a realistic determination, for example, the server electronic
device 901 may initialize the cumulative points of each cube with
third-party data about the human density of the environment 801.
The server electronic device 901 may also, in some embodiments,
utilize a Bayesian smoothing technique to ensure that these
probabilities match a realistic determination. For example, the
server electronic device 901 may initialize the cumulative points
of each "cube" with some small value proportional to its volume, as
illustrated by the equation set forth below:
cumulative points at first instance of ID=h(volume(y)), [0160] for
some monotonically Increasing h
[0161] In another embodiment, to ensure that these probabilities
match a realistic determination, the server electronic device 901
may generate and assign a penalty to, for example, the assumption
or the expectation that a particular client electronic device 916,
918, 920, 922, and 924 is in a particular location, as set forth by
the equation below:
Location loss(y,i)=f(prior(y,i)) [0162] for some monotonically
decreasing function, f
[0163] In certain embodiments, as previously discussed above,
whenever a geolocation estimate is completed, the server electronic
device 901 may calculate a residual by comparing the distance
implied by the geolocation estimate and the raw estimated distance.
For example, the residuals may include a constant stream of
supervised feedback upon which, for example, a sophisticated model
may be generated by the server electronic device 901. For example,
in certain embodiments, the server electronic device 901 may
generate a sophisticated model that may learn multiplicative,
linear, and complex biases based on the available context
information (e.g., the requesting device ID, the pinging device ID,
the time of day, the weather, the hardware settings, the make and
model of the hardware, and so forth). For example, in or more
embodiments, the server electronic device 901 may cause the model
to be trained online, and may thus provide access to a context-bias
adjusted estimated (CBAE) distance. The server electronic device
901 may then input the raw estimated distance and the known and
available context information. The generated model may then return
a prediction that is equal to, for example, the CBAE distance.
[0164] In some embodiments, for any two locations x and y (e.g.,
which may be represented as vectors in some embodiments), the
server electronic device 901 may determine an actual distance equal
to the Euclidean distance between the two locations x and y. For
example, as previously discussed above, for each possible message
type and for each possible location, the server electronic device
901 may store (e.g., utilizing "cubes" and/or enclosures) the
observed bias between the implied distance of the final geolocation
estimation and the implied distance of the measurement data.
Integrating these known biases from location x to location y, the
server electronic device 901 may calculate the location-bias
adjusted estimated (LBAE) distance. Thus, for any two points (x, y)
and any measurement m, the difference between LBAE(x, y) and
CBAE(m) may include the implied residual or error in measurement m,
given that the measuring client electronic device and pinging
client electronic device are located at locations x and y. In some
embodiments, in order to discover which assumed geolocations of the
client electronic devices 916, 918, 920, 922, and 924 minimize
measurement loss, the server electronic device 901 may define an
error function as set forth below:
measurement loss(x,y,m)=h(|CBAE(m)-LBAE(x,y)|CBAE(m)) [0165] for
some h( , ), monotonically increasing in the first argument
[0166] For example, in some embodiments, the server electronic
device 901 may define an error penalty associated with each of the
possible two locations x and y, for example, by comparing the
measurement expected based on both the known available context
(e.g., device make-model, device ID, weather, time, and so forth)
and the historical biases observed in the "cubes" and enclosures in
between locations x and y to the implied distance measurement. In
the above error function, the difference calculation is
proportional to the error calculation.
[0167] In certain embodiments, the client electronic devices 916,
918, 920, 922, and 924 may be utilized to process complex messages
when attempting to measure, for example, an object. For example,
the client electronic devices 916, 918, 920, 922, and 924 may each
include a camera or other data capturing device, which may, at
least in some embodiments, receive a 2-D image array corresponding
to the captured image of the measured object. In certain
embodiments, in order to assimilate these messages into the server
electronic device 901, the server electronic device 901 may rely
upon, for example, the possibility that all messages (e.g., both
relatively simple and more complex messages) may be compared to
other messages of the same type and saved in the database of the
server electronic device 901. Specifically, for each hardware type,
the server electronic device 901 may determine a similarity
function that compares the received message with the average
message received at a known location. Thus, the server electronic
device 901 may rely upon each hardware type having a
similarity-score function that compares the message information m
with any retrieved message from the database i, as illustrated by
the following equation:
similarity.sub.h(m,i) [0168] for each hardware type h [0169] with
range=[0,1]
[0170] In certain embodiments, for each hardware type, message, and
comparison message, the client electronic devices 916, 918, 920,
922, and 924 may estimate distance between, for example, the camera
and the image under the assumption that the measurement data
includes the comparison object. For example, given the aperture and
focal length of a camera of the client electronic devices 916, 918,
920, 922, and 924, and the known size of an object, the client
electronic devices 916, 918, 920, 922, and 924 may estimate the
distance between the object and the camera by its relative size
inside the image. In some embodiments, the server electronic device
901 may rely on each measuring hardware type (e.g., camera or other
data capturing device) having a distance estimation OE.sub.hi(m)
(e.g., object estimated [OE] distance) for each hardware type h and
each assumed object i. In one or more embodiments, the server
electronic device 901 may define measurement loss in this case
as:
measurement
loss(x,i,m)=min(f(similarity.sub.h(m,i)).sub.g(|OE.sub.h|i(m)-LBAE(x,y)|,-
OE.sub.h|i(m))) [0171] where y=the location of i [0172] where h is
the hardware of m [0173] for some monotonically increasing
functions f and g
[0174] For example, in certain embodiments, as may be ascertained
by the above equation, the server electronic device 901 may define
an error penalty associated with each possible client electronic
device 916, 918, 920, 922, and 924 location x and by comparing the
measurement expected based on the hardware formula and the
historical biases observed in the "cubes" and enclosures between
location x and the known location of the object y. In some
embodiments, possible types of error may include, for example, the
possibility that an object of interest was not the actual object
included in the measurement data, and the possibility that the
distance calculation between the camera or other data capturing
device of the client electronic device 916, 918, 920, 922, and 924
and the object is at least partially inaccurate.
[0175] Thus, the server electronic device 901 may determine a
compromise between the two possible errors by computing the error
(e.g., a unit-less value) of each, and then calculating and
utilizing the minimum of the two possible errors. In some
embodiments, if the server electronic device 901 determines that
the first possible error type is less than the other possible error
type, then the server electronic device 901 may determine that the
overall error is monotonically increasing in the similarity score
returned. Specifically, the server electronic device 901 may
determine that the penalty of rejecting the image or other captured
measurement data as correct is high when the similarity is high. On
the other hand, if the server electronic device 901 determines that
the second error possible type is less than the first possible
error type, then the server electronic device 901 may determine
that the overall error decreases when the estimated distance from
the objection information calculation OE is closer to the location
adjusted distance between the two locations x and y and increases
when these two values diverge.
[0176] In certain embodiments, the server electronic device 901 may
also estimate the velocities and accelerations of the client
electronic devices 916, 918, 920, 922, and 924 at various times.
Specifically, another type of error is the error incurred by
assuming that a particular client electronic device 916, 918, 920,
922, and 924 is at position x.sub.t1, and at time t.sub.1 and at
another position x.sub.t2 at time t.sub.2. For example, the server
electronic device 901 may determine that there should be a large
error incurred if x.sub.t1 and x.sub.t2 are far enough apart that
(t.sub.2-t.sub.1) and the historical velocity of the particular
client electronic device 916, 918, 920, 922, and 924 and the
historical velocity at or near this location. Specifically, for
each consecutive time t.sub.i-1, t.sub.1, and t.sub.1+i, the server
electronic device 901 may calculate velocity and acceleration error
of, for example, particular client electronic device 916, 918, 920,
922, and 924 as:
user velocity loss(v.sub.i,v.sub.i+1)=f(p(v.sub.i,v.sub.i+1|user
ID=x.sub.1)) [0177] for some decreasing function f
[0178] Similarly, the server electronic device 901 may calculate
velocity and acceleration error of, for example, particular client
electronic device 916, 918, 920, 922, and 924 as:
locution velocity
loss(v.sub.i,v.sub.i+1)=f(p(v.sub.i,v.sub.i+1|location=x.sub.1))
[0179] for some decreasing function f
[0180] In certain embodiments, the server electronic device 901 may
calculate specific geolocation estimates of any of the client
electronic device 916, 918, 920, 922, and 924. For example, in
certain embodiments, if the current time is T, then the server
electronic device 901 may consider all measurements by all devices
in the last delta(t) minutes, which delta (t) is a global lookback
parameter of the sever electronic device 901. For example, the
number of measurements may be finite, then there is a finite set of
times in [T-delta(t), T]. Having retrieved the set of all
measurements in the lookback window, and having the list of all
sending client electronic devices 916, 918, 920, 922, and 924 and
all possible pinging device IDs of client electronic devices 916,
918, 920, 922, and 924, the server electronic device 901 may
generate a geolocation estimate by solving for the estimated
geolocations of each of the client electronic devices 916, 918,
920, 922, and 924 concurrently.
[0181] In some embodiments, the server electronic device 901 may
also minimize, for example, the total error over all sets of
possible environment 801 positions of each client electronic
devices 916, 918, 920, 922, and 924. Specifically, in some
embodiments, the server electronic device 901 may declare the
position of each connection ID x X at each time t {T}, x.sub.t to
be a variable. The server electronic device 901 may then utilize,
for example, a non-linear optimization technique to calculate a
solution that minimizes the total error objective function (e.g.,
corresponding to a function of those variables). The server
electronic device 901 may keep track of all connection IDs used
within this lookback regardless of whether they are currently
active connections or not. In some embodiments, another variable in
the total error function is the probability that a particular
client electronic device 916, 918, 920, 922, and 924 with
connection ID c and device ID i, is the actual pinger for message m
(e.g., for each message m) in the current lookback window of the
type that may include a pinging device pinger(m, c). In one
embodiment, the server electronic device 901 may restrict pinger(m,
c) to only non-negative values (e.g., non-negative integers) to
ensure that pinger(m, c) may include a valid set of probabilities
(e.g., pinger(m, c)>0).
[0182] In certain embodiments, to account for the possibility that
an unknown occurrence caused the appearance of a ping, the server
electronic device 901 may calculate and assign a pinger loss
penalty, as set forth below:
pinger loss(m)=f.sub.h(1-.SIGMA..sub.cpinger(m,c)) [0183] for some
monotonically increasing function, f.sub.h [0184] where h is the
hardware of m
[0185] As may be ascertained from the above equation,
1-.SIGMA..sub.cpinger(m, c) may refer to the probability that an
unknown occurrence occurred, and the server electronic device 901
may assign an increasing penalty based on the unknown occurrence.
In certain embodiments, the penalty may vary according to hardware
type, as some types of hardware components may be more susceptible
to such an occurrence. Further, in some embodiments, if a
particular client electronic device 916, 918, 920, 922, and 924 is
known not to have the hardware necessary to perform a ping message,
then that device may not considered: (pinger(m, c)=0).
Additionally, if a message is of a hardware type that includes a
particular pinging client electronic device 916, 918, 920, 922, and
924 being instructed to ping, then the client electronic devices
916, 918, 920, 922, and 924 that do not meet a defined criteria may
not be considered.
[0186] For example, in one embodiment, the defined criteria may be
based on whether the particular pinging client electronic device
916, 918, 920, 922, and 924, for example, received an instruction
to ping on a compatible hardware setting before the message
timestamp within a lookback horizon. In another embodiment, the
defined criteria may be based on whether either the last report
received by the particular pinging client electronic device 916,
918, 920, 922, and 924 was before the instruction, or whether, for
example, the first report received by the server electronic device
901 after the instruction was also after the message timestamp. The
defined criteria may thus prevent any client electronic device that
could not have been the particular pinging client electronic device
916, 918, 920, 922, and 924 from being attributed any probability
density in the solution to the total-loss function. Another
variable in the total error function may include the probability
that object o was the measured object for message m (e.g., for each
message m in the current lookback window) of the type that include
an object placement (m, o). In one embodiment, the server
electronic device 901 may restrict placement (m, o) to only
non-negative values (e.g., non-negative integers) to ensure that
placement (m, o) may include a valid set of probabilities (e.g.,
placement(m, o)>0).
[0187] In certain embodiments, to account for the possibility that
an unknown occurrence caused the appearance of a ping, the server
electronic device 901 may calculate and assign a placement loss
penalty, as set forth below:
placement loss=f.sub.h(1-.SIGMA..sub.oplacement(m,o)) [0188] for
some monotonically increasing function, f.sub.h [0189] where h is
the hardware of m
[0190] In certain embodiments, as may be ascertained from the above
equation, 1-.SIGMA..sub.0 placement(m, o) may refer to the
probability that an unknown occurrence occurred, and the server
electronic device 901 may assign an increasing penalty based on the
unknown occurrence. As discussed above with respect to the
pinger(m, c), in certain embodiments, the penalty may vary
according to hardware type, as some types of hardware components
may be more susceptible to such an occurrence. Further, in some
embodiments, if an object is known not to have the properties
necessary, then that object is not considered: (placement(m, o)=0).
This may thus prevent any object that could not have been the
detected object from being attributed any probability density in
the solution to the total-loss function calculated by the server
electronic device 901.
[0191] In certain embodiments, with the variables X, pinger (m, c),
and placement(m, o) defined, the server electronic device 901 may
calculate the total loss as:
total loss=.SIGMA..sub.t.di-elect cons.Tloss.sub.t
loss.sub.t=pinger(m.sub.t,c)*measurement
loss(x.sub.t.sup.c,x.sub.t.sup.s,m.sub.t)+ . . .
+.SIGMA..sub.oplacement(m.sub.t,o)*measurement
loss(y.sup.i,x.sub.t.sup.s, m.sub.t)+ . . . +.SIGMA..sub.clocation
loss(i.sup.c,x.sub.t.sup.c)+ . . . +user velocity
loss(v.sub.t-1,v.sub.t)+ . . . +location velocity
loss(v.sub.t-1,v.sub.t)+ . . . +pinger loss(m.sub.t)
where the sender of message m, is s, and is known [0192] where the
device ID of device with connection ID=c is i.sup.c, and is known
[0193] where velocity losses are zero when t-1T
[0194] Thus, the server electronic device 901 may calculate the
approximate geolocation of each of the client electronic devices
916, 918, 920, 922, and 924. In one embodiment, the server
electronic device 901 may minimize the above function subject to
the following constraints:
pinger(m.sub.t,c).gtoreq.0
placement(m.sub.t,o).gtoreq.0
.SIGMA..sub.oplacement(m,o).ltoreq.1
.SIGMA..sub.opinger(m, o).ltoreq.1 [0195] plager(m.sub.t, c)=0,
when infeasible [0196] placement(m.sub.t,o)=0, when Infeasible
[0197] The method 1000 may continue with the one or more computer
processing devices of the server electronic device generating a
geolocation confidence value (Step 1012). Specifically, the
geolocation estimate may constitute only part of the delivery
message. In certain embodiments, the server electronic device 901
may also calculate a geolocation confidence level of the
geolocation estimate. In one embodiment, the server electronic
device 901 may convey confidence as a confidence interval. For
example, a distance d and a probability p combined together by the
server electronic device 901 may indicate that the geolocation
estimate is accurate enough that p percent of similar geolocation
estimates are within d distance of the actual geolocation of the
client electronic devices 916, 918, 920, 922, and 924.
[0198] In some embodiments, the server electronic device 901 may
calculate the geolocation confidence level by monitoring, for
example, the gradient of the total loss function. For example, the
gradient of a function may include the rate at which the value
changes as one of its variables is moved or varied. Specifically,
when the gradient of the total loss function is small, the server
electronic device 901 may determine that points nearby the
estimated geolocation are only slightly less likely than the
geolocation estimate itself. In such a case, the server electronic
device 901 may determine a low level of confidence. In some
embodiments, the server electronic device 901 may link what has
been determined from the gradient with what is desired, for
example, by a user, and generate a confidence interval or other
form of confidence report. The below equation illustrates the
foregoing:
p x = ( .differential. .differential. x Total Loss , d x )
##EQU00004## where d x is the requested delivery radius of device x
##EQU00004.2## for some function g , monotonically increasing in
the first argument and ##EQU00004.3## decreasing in the second
##EQU00004.4##
[0199] For example, referring to the equation above, the function g
may be improved by selecting the best functions from a family of
functions G. The server electronic device 901 may determine which
function is best by, for example, adding the client electronic
devices 916, 918, 920, 922, and 924 at known locations and ranking
possible candidate function g, or, in another embodiment,
instructing the client electronic devices 916, 918, 920, 922, and
924 with known locations to rank possible candidate function g.
[0200] The method 1000 may then conclude with the one or more
computer processing devices of the server electronic device
transmitting the geolocation estimation data and the geolocation
confidence value to one or more of the client electronic devices
916, 918, 920, 922, and 924 (Step 1014) indicating, for example,
the approximate geolocation of each of the client electronic
devices 916, 918, 920, 922, and 924 within the environment 801 and
a geolocation confidence value indicating, for example, the
confidence level that the geolocation estimation is the actual
geolocation of each of the client electronic devices 916, 918, 920,
922, and 924. In this way, the present embodiments may provide
techniques to efficiently determine and track the approximate
geolocation of electronic devices within or about indoor and
outdoor environments, or otherwise in any of various environments
in which large-scale satellite systems may be inaccurate,
imprecise, or otherwise unavailable.
[0201] Turning now to FIG. 11, which illustrates is a flow diagram
of a method 1100 of determining and tracking the approximate
geolocation devices, and, more specifically, of calculating
geolocation estimation data for the electronic devices in
accordance with the present embodiments. Similarly as discussed
above with respect to the method 1000 of FIG. 10, the method 1100
may also be performed by processing logic that may include hardware
such as one or more computer processing devices, software (e.g.,
instructions running/executing on a computer processing device),
firmware (e.g., microcode), or a combination thereof, such as the
server electronic device 901 discussed above with respect to FIG.
9.
[0202] The method 1100 may begin with one or more computer
processing devices of the server electronic device identifying one
or more client electronic devices of a plurality of client
electronic devices to be located (Step 1102). The method 1100 may
continue with one or more computer processing devices of the server
electronic device generating geolocation estimation data and a
geolocation confidence value with respect to the one or more client
electronic devices based on, for example, at least one geolocation
estimation model (Step 1104). For example, as previously discussed
above with respect to method 1000 of FIG. 10, the server electronic
device 901 may determine an approximate physical location of one or
more client electronic devices 916, 918, 920, 922, and 924 based on
a device ID of the one or more client electronic devices 916, 918,
920, 922, and 924 in accordance with a historical distribution,
based on approximate distances measured between each of a number
the client electronic devices 916, 918, 920, 922, and 924 with
respect to each other or with respect to one or more known physical
obstructions or constructions (e.g., walls, islands, columns,
pillars, and so forth), or based on an estimated velocity and an
acceleration of the client electronic devices 916, 918, 920, 922,
and 924.
[0203] The method 1100 may continue with one or more computer
processing devices of the server electronic device determining an
approximate physical location of the client electronic device based
on the calculated geolocation estimation data and the calculated
geolocation confidence value (Step 1106) (e.g., as generally
discussed above with respect to FIG. 10). The method 1100 may then
conclude with one or more computer processing devices of the server
electronic device storing the geolocation estimation data and the
geolocation confidence value (Step 1108).
[0204] Turning now to FIG. 12, which illustrates is a flow diagram
of a method 1200 of determining and tracking the approximate
geolocation of electronic devices, and, more specifically, a method
for receiving calculated geolocation estimation data at a client
electronic device in accordance with the present embodiments. The
method 1200 may be performed by processing logic that may include
hardware such as one or more computer processing devices, software
(e.g., instructions running/executing on a computer processing
device), firmware (e.g., microcode), or a combination thereof, such
as the server electronic device 901 discussed above with respect to
FIG. 9.
[0205] The method 1200 may begin with one or more processing
devices of a client electronic device receiving desired operational
parameters on the client electronic device (Step 1202). The method
1200 may continue with one or more computer processing devices of a
client electronic device transmitting the desired operational
parameters to a server electronic device (Step 1204) (e.g., as
generally discussed above with respect to FIG. 10). The method 1200
may then continue with one or more computer processing devices of a
client electronic device receiving measurement instructions from
the server electronic device based on the desired operational
parameters (Step 1206). The method 1200 may then continue with one
or more computer processing devices of a client electronic device
transmitting the measurement instructions to at least a subset of a
plurality of other client electronic devices (Step 1208) (e.g., as
generally discussed above with respect to FIG. 10).
[0206] The method 1200 may then continue with one or more computer
processing devices of a client electronic device generating
measurement data based on the measurement instructions received
from the server electronic device (Step 1210). The method 1200 may
then continue with one or more computer processing devices of a
client electronic device transmitting the measurement data to the
server electronic device (Step 1212). The method 1200 may then
conclude with one or more computer processing devices of a client
electronic device receiving geolocation estimation data and a
geolocation confidence value from the server electronic device in
response to the measurement data (Step 1214) (e.g., as generally
discussed above with respect to FIG. 10). As previously noted, the
present embodiments may provide techniques to efficiently determine
and track the approximate geolocation of electronic devices within
indoor environments, outdoor environment, or otherwise in any of
various environments in which, for example, large-scale satellite
systems such as, GPS may be inaccurate or imprecise.
[0207] FIG. 13 is a block diagram of an example computing device
1300 that may perform one or more of the operations described
herein, in accordance with the present embodiments. The computing
device 1300 may be connected to other computing devices in a LAN,
an intranet, an extranet, and/or the Internet. The computing device
1300 may operate in the capacity of a server machine in
client-server network environment or in the capacity of a client in
a peer-to-peer network environment. The computing device 1300 may
be provided by a personal computer (PC), a set-top box (STB), a
server, a network router, switch or bridge, or any machine capable
of executing a set of instructions (sequential or otherwise) that
specify actions to be taken by that machine. Further, while only a
single computing device 1300 is illustrated, the term "computing
device" shall also be taken to include any collection of computing
devices that individually or jointly execute a set (or multiple
sets) of instructions to perform the methods discussed herein.
[0208] The example computing device 1300 may include a computer
processing device (e.g., a general purpose processor, ASIC, etc.)
1302, a main memory 1304, a static memory 506 (e.g., flash memory
and a data storage device 1308), which may communicate with each
other via a bus 1310. The computer processing device 1302 may be
provided by one or more general-purpose processing devices such as
a microprocessor, central processing unit, or the like. In an
illustrative example, computer processing device 1302 may comprise
a complex instruction set computing (CISC) microprocessor, reduced
instruction set computing (RISC) microprocessor, very long
instruction word (VLIW) microprocessor, or a processor implementing
other instruction sets or processors implementing a combination of
instruction sets. The computer processing device 1302 may also
comprise one or more special-purpose processing devices such as an
application specific integrated circuit (ASIC), a field
programmable gate array (FPGA), a digital signal processor (DSP),
network processor, or the like. The computer processing device 1302
may be configured to execute the operations described herein, in
accordance with one or more aspects of the present disclosure, for
performing the operations and steps discussed herein.
[0209] The computing device 1300 may further include a network
interface device 1312, which may communicate with a network 1314.
The data storage device 1308 may include a machine-readable storage
medium 1316 on which may be stored one or more sets of
instructions, e.g., instructions for carrying out the operations
described herein, in accordance with one or more aspects of the
present disclosure. Instructions implementing module 1318 may also
reside, completely or at least partially, within main memory 1304
and/or within computer processing device 1302 during execution
thereof by the computing device 1300, main memory 1304 and computer
processing device 1302 also constituting computer-readable media.
The instructions may further be transmitted or received over the
network 1314 via the network interface device 1312.
[0210] While machine-readable storage medium 1316 is shown in an
illustrative example to be a single medium, the term
"computer-readable storage medium" should be taken to include a
single medium or multiple media (e.g., a centralized or distributed
database and/or associated caches and servers) that store the one
or more sets of instructions. The term "computer-readable storage
medium" shall also be taken to include any medium that is capable
of storing, encoding or carrying a set of instructions for
execution by the machine and that cause the machine to perform the
methods described herein. The term "computer-readable storage
medium" shall accordingly be taken to include, but not be limited
to, solid-state memories, optical media and magnetic media.
[0211] Embodiments of the subject matter and the operations
described in this specification can be implemented in digital
electronic circuitry, or in computer software, firmware, or
hardware, including the structures disclosed in this specification
and their structural equivalents, or in combinations of one or more
of them. Embodiments of the subject matter described in this
specification can be implemented as one or more computer programs,
i.e., one or more modules of computer program instructions, encoded
on computer storage medium for execution by, or to control the
operation of, data processing apparatus. Alternatively, or in
addition, the program instructions can be encoded on an
artificially-generated propagated signal, e.g., a machine-generated
electrical, optical, or electromagnetic signal that is generated to
encode information for transmission to suitable receiver apparatus
for execution by a data processing apparatus. A computer storage
medium can be, or be included in, a computer-readable storage
device, a computer-readable storage substrate, a random or serial
access memory array or device, or a combination of one or more of
them. Moreover, while a computer storage medium is not a propagated
signal, a computer storage medium can be a source or destination of
computer program instructions encoded in an artificially-generated
propagated signal. The computer storage medium can also be, or be
included in, one or more separate physical components or media
(e.g., multiple CDs, disks, or other storage devices).
[0212] The operations described in this specification can be
implemented as operations performed by a data processing apparatus
on data stored on one or more computer-readable storage devices or
received from other sources.
[0213] The term "computer processing device" encompasses all kinds
of apparatus, devices, and machines for processing data, including
by way of example a programmable processor, a computer, a system on
a chip, or multiple ones, or combinations, of the foregoing.
Although referred to as a computer processing device, use of the
term also encompasses embodiments that include one or more computer
processing devices. The computer processing device can include
special purpose logic circuitry, e.g., an FPGA (field programmable
gate array) or an ASIC (application-specific integrated circuit).
The computer processing device can also include, in addition to
hardware, code that creates an execution environment for the
computer program in question, e.g., code that constitutes processor
firmware, a protocol stack, a database management system, an
operating system, a cross-platform runtime environment, a virtual
machine, or a combination of one or more of them. The computer
processing device and execution environment can realize various
different computing model infrastructures, such as web services,
distributed computing and grid computing infrastructures.
[0214] A computer program (also known as a program, software,
software application, script, or code) can be written in any form
of programming language, including compiled or interpreted
languages, declarative, procedural, or functional languages, and it
can be deployed in any form, including as a stand-alone program or
as a module, component, subroutine, object, or other unit suitable
for use in a computing environment. A computer program may, but
need not, correspond to a file in a file system. A program can be
stored in a portion of a file that holds other programs or data
(e.g., one or more scripts stored in a markup language resource),
in a single file dedicated to the program in question, or in
multiple coordinated files (e.g., files that store one or more
modules, sub-programs, or portions of code). A computer program can
be deployed to be executed on one computer or on multiple computers
that are located at one site or distributed across multiple sites
and interconnected by a communication network.
[0215] The processes and logic flows described in this
specification can be performed by one or more programmable
processors executing one or more computer programs to perform
actions by operating on input data and generating output. The
processes and logic flows can also be performed by, and apparatus
can also be implemented as, special purpose logic circuitry, e.g.,
an FPGA (field programmable gate array) or an ASIC
(application-specific integrated circuit).
[0216] Processing devices suitable for the execution of a computer
program include, by way of example, both general and special
purpose microprocessors, and any one or more processors of any kind
of digital computer. Generally, a processing device will receive
instructions and data from a read-only memory or a random access
memory or both. The essential elements of a computer are a
processor for performing actions in accordance with instructions
and one or more memory devices for storing instructions and data.
Generally, a computer will also include, or be operatively coupled
to receive data from or transfer data to, or both, one or more mass
storage devices for storing data, e.g., magnetic disks,
magneto-optical disks, optical disks, or solid state drives.
However, a computer need not have such devices. Moreover, a
computer can be embedded in another device, e.g., a smart phone, a
mobile audio or video player, a game console, a Global Positioning
System (GPS) receiver, or a portable storage device (e.g., a
universal serial bus (USB) flash drive), to name just a few.
Devices suitable for storing computer program instructions and data
include all forms of non-volatile memory, media and memory devices,
including, by way of example, semiconductor memory devices, e.g.,
EPROM, EEPROM, and flash memory devices; magnetic disks, e.g.,
internal hard disks or removable disks; magneto-optical disks; and
CD-ROM and DVD-ROM disks. The processing device and the memory can
be supplemented by, or incorporated in, special purpose logic
circuitry.
[0217] To provide for interaction with a user, embodiments of the
subject matter described in this specification can be implemented
on a computer having a display device, e.g., a CRT (cathode ray
tube) or LCD (liquid crystal display) monitor, for displaying
information to the user and a keyboard and a pointing device, e.g.,
a mouse, a trackball, a touchpad, or a stylus, by which the user
can provide input to the computer. Other kinds of devices can be
used to provide for interaction with a user as well; for example,
feedback provided to the user can be any form of sensory feedback,
e.g., visual feedback, auditory feedback, or tactile feedback; and
input from the user can be received in any form, including
acoustic, speech, or tactile input. In addition, a computer can
interact with a user by sending resources to and receiving
resources from a device that is used by the user; for example, by
sending web pages to a web browser on a user's client device in
response to requests received from the web browser.
[0218] Embodiments of the subject matter described in this
specification can be implemented in a computing system that
includes a back-end component, e.g., as a data server, or that
includes a middleware component, e.g., an application server, or
that includes a front-end component, e.g., a client computer having
a graphical user interface or a Web browser through which a user
can interact with an implementation of the subject matter described
in this specification, or any combination of one or more such
back-end, middleware, or front-end components. The components of
the system can be interconnected by any form or medium of digital
data communication, e.g., a communication network. Examples of
communication networks include a local area network ("LAN") and a
wide area network ("WAN"), an inter-network (e.g., the Internet),
and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
[0219] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other. In some embodiments, a
server transmits data (e.g., an HTML page) to a client device
(e.g., for purposes of displaying data to and receiving user input
from a user interacting with the client device). Data generated at
the client device (e.g., a result of the user interaction) can be
received from the client device at the server.
[0220] A system of one or more computers can be configured to
perform particular operations or actions by virtue of having
software, firmware, hardware, or a combination of them installed on
the system that in operation causes or cause the system to perform
the actions. One or more computer programs can be configured to
perform particular operations or actions by virtue of including
instructions that, when executed by data processing apparatus,
cause the apparatus to perform the actions.
[0221] While this specification contains many specific
implementation details, these should not be construed as
limitations on the scope of any inventions or of what may be
claimed, but rather as descriptions of features specific to
particular embodiments of particular inventions. Certain features
that are described in this specification in the context of separate
embodiments can also be implemented in combination in a single
embodiment. Conversely, various features that are described in the
context of a single embodiment can also be implemented in multiple
embodiments separately or in any suitable subcombination. Moreover,
although features may be described above as acting in certain
combinations and even initially claimed as such, one or more
features from a claimed combination can in some cases be excised
from the combination, and the claimed combination may be directed
to a subcombination or variation of a subcombination.
[0222] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. In certain circumstances,
multitasking and parallel processing may be advantageous. Moreover,
the separation of various system components in the embodiments
described above should not be understood as requiring such
separation in all embodiments, and it should be understood that the
described program components and systems can generally be
integrated together in a single software product or packaged into
multiple software products.
[0223] Thus, particular embodiments of the subject matter have been
described. Other embodiments are within the scope of the following
claims. In some cases, the actions recited in the claims can be
performed in a different order and still achieve desirable results.
In addition, the processes depicted in the accompanying figures do
not necessarily require the particular order shown, or sequential
order, to achieve desirable results. In certain implementations,
multitasking and parallel processing may be advantageous.
* * * * *