U.S. patent application number 15/804294 was filed with the patent office on 2018-03-15 for computer-implemented systems utilizing sensor networks for sensing temperature and motion environmental parameters; and methods of use thereof.
The applicant listed for this patent is Triplay, Inc.. Invention is credited to Edward K.Y. Jung, Clarence T. Tegreene.
Application Number | 20180075072 15/804294 |
Document ID | / |
Family ID | 35944052 |
Filed Date | 2018-03-15 |
United States Patent
Application |
20180075072 |
Kind Code |
A1 |
Jung; Edward K.Y. ; et
al. |
March 15, 2018 |
COMPUTER-IMPLEMENTED SYSTEMS UTILIZING SENSOR NETWORKS FOR SENSING
TEMPERATURE AND MOTION ENVIRONMENTAL PARAMETERS; AND METHODS OF USE
THEREOF
Abstract
Computer-implemented systems utilizing sensor networks for
sensing temperature and motion environmental parameters, and
performing at least operations of electronically establishing,
based on pattern recognition criteria, correspondence of a
plurality of representative features a plurality of characteristics
of an occurrence, where a first instance of the occurrence occurred
within a first time period of a plurality of time periods;
electronically discovering, based on the correspondence, a second
instance of the occurrence in an environment during a second time
period of the plurality of time periods; and electronically
causing, based on the discovery of the second instance of the
occurrence, a change in the environment via an
electronically-controlled device.
Inventors: |
Jung; Edward K.Y.;
(Bellevue, WA) ; Tegreene; Clarence T.; (Bellevue,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Triplay, Inc. |
New York |
NY |
US |
|
|
Family ID: |
35944052 |
Appl. No.: |
15/804294 |
Filed: |
November 6, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15425564 |
Feb 6, 2017 |
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15804294 |
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15043328 |
Feb 12, 2016 |
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15425564 |
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10909200 |
Jul 30, 2004 |
9261383 |
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15043328 |
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10903692 |
Jul 30, 2004 |
7457834 |
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10909200 |
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10903652 |
Jul 30, 2004 |
7536388 |
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10903692 |
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10816375 |
Mar 31, 2004 |
8200744 |
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10903652 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 5/02 20130101; G06N
20/00 20190101; G06F 16/22 20190101; G06N 5/047 20130101; H04Q 9/00
20130101; H04Q 2209/20 20130101; G08B 29/188 20130101; H04Q 2209/40
20130101; G06N 3/08 20130101; H04W 4/38 20180201; G01D 9/005
20130101; H04W 84/18 20130101; H04L 67/12 20130101; G08B 29/186
20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; H04L 29/08 20060101 H04L029/08 |
Claims
1. A computer-implemented method, comprising: receiving sensed data
of at least one parameter from a sensor node over a network,
wherein the at least one sensor network comprises a plurality of
remotely located recording sensor nodes, wherein each of the
plurality of remotely located recording sensor nodes captures the
environmental data; storing continuously the received sensed data
as a first sensor data set in a storage device; receiving an input
selection from an input-selector of a target-event having at least
one representative feature; selecting a pattern recognition
criteria corresponding to the at least one representative feature
of the target-event; searching, in response to the input selection
corresponding to the target-event, for sensor data correlating to
the at least one representative feature using the selected pattern
recognition criteria; and determining if the sensor data
correlating to the at least one representative feature is found: if
not found, continuously deleting the sensed data of the at least
one parameter from the first sensor data set; and if found, storing
the sensor data correlating to the at least one representative
feature in a retained data storage configured to store
representative features of the target-event and continuously
deleting the sensed data of the at least one parameter from the
first sensor data set.
Description
RELATED APPLICATIONS
[0001] The present application is related to, claims the earliest
available effective filing date(s) from the following listed
application(s) (the "Related Application(s)") (e.g., claims
earliest available priority dates for other than provisional patent
applications or claims benefits under 35 USC .sctn. 119(e) for
provisional patent applications), and incorporates by reference in
its entirety all subject matter of the following listed
application(s); the present application also claims the earliest
available effective filing date(s) from, and also incorporates by
reference in its entirety all subject matter of any and all parent,
grandparent, great-grandparent, etc. applications of the Related
Application(s)".
[0002] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation of U.S. patent
application Ser. No. 10/909,200, filed entitled DISCOVERY OF
OCCURRENCE-DATA, naming Edward K. Y. Jung and Clarence T. Tegreene
as inventors, now issued U.S. Pat. No. 9,261,383.
[0003] For purposes of the USPTO extra-statutory requirements, U.S.
patent application Ser. No. 10/909,200, constitutes a
continuation-in-part of U.S. patent application Ser. No.
10/903,692, filed Jul. 30, 2004, entitled AGGREGATION AND RETRIEVAL
OF NETWORK SENSOR DATA, naming Edward K. Y. Jung and Clarence T.
Tegreene as inventors, now issued U.S. Pat. No. 7,457,834.
[0004] For purposes of the USPTO extra-statutory requirements, U.S.
patent application Ser. No. 10/909,200, constitutes a
continuation-in-part of U.S. patent application Ser. No.
10/903,652, filed Jul. 30, 2004, entitled AGGREGATION AND RETRIEVAL
OF NETWORK SENSOR DATA, naming Edward K. Y. Jung and Clarence T.
Tegreene as inventors, now issued U.S. Pat. No. 7,536,388.
BACKGROUND
[0005] The present era of computing has introduced an array of
small devices that perform a variety of specific functions.
Cellular phones, pagers and portable digital assistants are common
examples of these. As technology progresses, however, devices will
continue to become smaller and more specialized. One class of small
device that is beginning to emerge is a tiny, sensor, sometimes
known as a "mote" that is often implemented in a networked
configuration.
[0006] Networked sensor nodes, sometimes referred to as sensor
devices, are undergoing significant advances in structure and low
power technology. In some applications, sensor nodes may utilize
micro-electromechanical systems, or MEMS, technology. Sensor nodes
may include more than one component, such as an embedded processor,
digital storage, power source, a transceiver, and an array of
sensors, environmental detectors, and/or actuators. In some cases,
sensor nodes may rely on small batteries, solar-powered cell, or
ambient energy for power, and run for long periods of time without
maintenance.
[0007] Communication characteristics of nodes may be determined by
physical design characteristics and intended use scenarios or both.
In some applications, sensor nodes may act as a data source, and it
may also forward data from other sensors that are out of range of a
central station.
[0008] The practical applications of such mini-devices range from
environmental monitoring to micro-robots capable of performing
microscopic scale tasks. While functionality of an individual
sensor node may be limited, a grouping of nodes working together
can accomplish a range of tasks, including high level tasks. The
tasks of a grouping may include operations such as general
information gathering, security, industrial monitoring, military
reconnaissance, or biomedical monitoring.
[0009] The integration of computation, storage, communication, and
physical interaction in silicon has shrunk some sensor nodes down
to microscopic scales. The ability to create sensors and actuators
with IC technology and integrate them with computational logic has
created an abundance of low-power, tiny sensor nodes. Combining
these tiny sensor nodes with low power wireless communication
networks aids in developing economical, distributed sensors
networks. The number of sensor nodes used in a network is
increasing as their cost decreases and functionality increases. As
a result, the sheer volume of data created by sensor networks,
particularly distributed sensor networks, is rapidly
increasing.
SUMMARY
[0010] An embodiment provides an occurrence-data retrieval system.
The system includes a data storage operable to store a plurality of
instances of occurrence-data, each instance of the occurrence-data
having a representative feature, a central computing device
operable to communicate with the data storage, and instructions
that cause a computing device to perform steps. The steps include
receive from an input-selector an input selection corresponding to
a target-occurrence having a representative feature, and select a
pattern recognition criteria corresponding to the representative
feature of the target-occurrence. The steps also include
automatically search the plurality of instances of stored
occurrence-data for data correlating to the target-occurrence using
the selected pattern recognition criteria, and provide an output
indicative of a result of the automatic search. The input-selector
may include an individual user. The pattern recognition criteria
may be automatically selected in response to input selection
corresponding to the target-occurrence.
[0011] The input selection may further include a representative
feature of the target-occurrence. The representative feature may
include a time period. The representative feature may include
acoustic frequency components. The representative feature may
include a frequency pattern. The frequency pattern may include at
least one selected from a group consisting of a recognized word, a
set of words, a breaking glass, a dog bark, a door opening, an
alarm, a threshold acoustic level, and a voiceprint. The
representative feature may include an electromagnetic pattern. The
electromagnetic pattern may include at least one selected from a
group consisting of a visible light, an infrared light, an
ultraviolet light, and a radar. The recognition criteria may be
automatically selected in response to the selected representative
feature. The automatic search instruction may include using the
pattern recognition criteria selected in response to the inputted
representative feature. The instruction to provide an output may
include provide an instance of the correlating occurrence-data. The
correlating occurrence-data provided may include a segment of the
correlating occurrence-data. The instruction to provide an output
may include provide a degraded representation of an instance of the
correlating occurrence-data. The instruction to provide an output
may include provide an instance of non-correlating occurrence-data.
The non-correlating occurrence-data provided may include a degraded
representation of the non-correlating occurrence-data. The
occurrence-data may include sensor data generated by a plurality of
networked remote sensor devices. The instructions may include
protect the plurality of instances of occurrence-data stored in the
data storage from unauthorized access. The data storage may include
a digital data storage device. Each instance of occurrence-data may
include a data sequence, and the data sequence may include a
chronological data sequence.
[0012] Another embodiment provides an occurrence-data retrieval
system. The system includes a computing device operable to
communicate with a data storage device. The data storage device is
operable to store a plurality of instances of occurrence-data from
remote data storages. Each instance of occurrence-data including a
representative feature sensed respectively by a device associated
with the remote data storage. The system also includes an
information security measure that protects instances of
occurrence-data stored in the data storage device from unauthorized
access, and instructions, which when implemented in a computing
device, cause the computing device to perform steps. The steps
include receive from an input-selector an input selection
corresponding to a target-occurrence having a representative
feature, a recipient selection, and a tendered access
authorization. In response to the tendered access authorization,
determine if at least one of the input-selector and recipient have
an access right. Also, automatically select a pattern recognition
criteria corresponding to at least one representative feature of
the target-occurrence, and in response to the input selection
corresponding to the target-occurrence, automatically search the
plurality of instances of occurrence-data stored in the data
storage device for data correlating to the target-occurrence using
the selected pattern recognition criteria. If at least one of the
input-selector and recipient have an access right, provide an
output indicative of a result of the automatic search to the
recipient.
[0013] The input-selector may include an individual user. The
input-selector and the recipient may be a same party. The recipient
may be an individual user. The information security measure may be
associated with the data storage device. The information security
measure may include an application associated with the computing
device. The data storage device may include at least one device
selected from a group consisting of a local data storage device and
a remote data storage device. The data storage device may include a
portable digital data storage device. The instruction to provide an
output indicative of a result may include provide the correlating
occurrence-data to the recipient. The steps of the instructions may
include receive a redaction selection, and a tender of a redaction
authorization, and determine if a redaction right is possessed. In
response to the redaction selection and a determination that a
redaction right is possessed, redact an instance of the plurality
of instances of occurrence-data from the data storage device. The
redaction selection may be received from at least one of the
input-selector and the recipient. The redacted instance of
occurrence-data may correlate to the target-occurrence
representative feature. The redacted instance of occurrence-data
may not correlate to the target-occurrence representative
feature.
[0014] A further embodiment provides an occurrence-data retrieval
system. The system includes a computing device operable to
communicate with a data storage device. The data storage device is
operable to store a plurality of instances of occurrence-data from
remote data storages. Each instance of occurrence-data having a
representative feature sensed respectively by a device associated
with the remote data storage. The system also includes an
information security measure that protects instances of
occurrence-data stored in the data storage device from unauthorized
access, and instructions that cause a computing device to perform
steps. The steps include receive from a redaction-selector a
redaction selection corresponding to a target-occurrence having a
representative feature, and a tender of a redaction authorization.
In response to the tendered redaction authorization, determine if
the redaction-selector possess a redaction right. Automatically
select a pattern recognition criteria corresponding to the
representative feature of the target-occurrence, and automatically
search the plurality of instances of occurrence-data stored in the
data storage device for data correlating to the target-occurrence
using the selected pattern recognition criteria. If the
redaction-selector possesses a redaction right, redact an instance
of the plurality of instances of occurrence-data from the data
storage device. The redacted instance of occurrence-data may
correlate to the target-occurrence representative feature. The
redacted instance of occurrence-data may not correlate to the
target-occurrence representative feature. The instructions may
include computer program instructions.
[0015] An embodiment provides a method implemented in a computing
device. The method includes receiving an input selection from an
input-selector, the input selection corresponding to a
target-occurrence having a representative feature, and selecting a
pattern recognition criteria corresponding to the representative
feature of the target-occurrence. In response to the input
selection corresponding to the target-occurrence, automatically
searching a plurality of instances of occurrence-data stored in a
data storage device for data correlating to the target-occurrence
representative feature using the selected pattern recognition
criteria. Each instance of the occurrence-data includes a
representative feature. Also, provide an output indicative of the
search results. The pattern recognition criteria may be
automatically selected in response to the target-occurrence. The
input selection may include selection of a representative feature
of the target-occurrence. The pattern recognition criteria may be
automatically selected in response to the input-selector selected
representative feature. The automatically searching step may use
the pattern recognition criteria selected in response to the
input-selector selected representative feature.
[0016] The providing an output may include providing an instance of
the correlating occurrence-data. The provided instance of
correlating occurrence-data may include a degraded representation
of the correlating occurrence-data. Alternatively, the provided
instance of correlating occurrence-data may include all data
associated with the correlating occurrence. The provided
correlating occurrence-data may include a segment of the
correlating occurrence-data. The providing an output may include
providing an instance of non-correlating occurrence-data. The
instance of correlating occurrence-data may include a degraded
representation of the non-correlating occurrence-data. The
occurrence-data may include sensor data generated by a plurality of
networked sensor devices.
[0017] Another embodiment provides a method implemented in a
computing device. The method includes receiving from an
input-selector an input selection corresponding to a
target-occurrence having a representative feature, and selecting a
filter corresponding to the representative feature of the
target-occurrence. Also, using the selected filter, automatically
filtering a plurality of instances of occurrence-data stored in a
data set for data correlating to the target-occurrence
representative feature, each instance of the occurrence-data having
a representative feature. The method includes providing an output
responsive to the filtering. The providing an output may include
providing an instance of occurrence-data correlating to a
target-occurrence representative feature, and may include storing
the instance of occurrence-data correlating to a target-occurrence
representative feature. The providing an output may include
providing an instance of occurrence-data not correlating to a
target-occurrence representative feature, and may include storing
the instance of occurrence-data not correlating to a
target-occurrence representative feature.
[0018] A further embodiment provides a method. The method includes
inputting a selection to a computing device corresponding to a
target-occurrence having a representative feature, and inputting a
selection to the computing device corresponding to a plurality of
instances of occurrence-data obtained from remote data storages.
Each instance of the occurrence-data includes a representative
feature sensed respectively by a device associated with the remote
data storage. In response to the input selection, receiving an
instance of occurrence-data correlating to the target-occurrence
from the computing device. The plurality of instances of
occurrence-data may be stored in a data storage device local to the
computing device. The received instance of occurrence-data may
include a feature correlating to a target-occurrence representative
feature automatically selected by the computing device. The input
selection corresponding to the target-occurrence may include
selection of a representative feature of the target-occurrence. The
received instance of occurrence-data may include an instance of
occurrence-data having a feature correlating to the selected
target-occurrence representative feature.
[0019] An embodiment provides a method implemented in a computing
device. The method includes receiving an input selection from an
input-selector, the input selection corresponding to a
target-occurrence having a representative feature, a recipient
selection, and a tendered access authorization. In response to the
tendered access authorization, determining if at least one of the
input-selector and the recipient possess an access right to a
plurality of instances of stored occurrence-data protected by an
information security measure. Each instance of occurrence-data
originating from remote data storages, having a representative
feature sensed respectively by a device associated with the remote
data storage, and respectively correlating to an occurrence. Also,
automatically selecting a pattern recognition criteria
corresponding to the representative feature of the
target-occurrence. In response to the input selection corresponding
to the target-occurrence, automatically searching the plurality of
instances of stored occurrence-data for data correlating to the
representative feature of the target-occurrence using the selected
pattern recognition criteria. If at least one of the input-selector
and recipient posses an access right, providing an output
indicative of a result of the automatic search to the recipient.
The occurrence-data may be stored in a data storage device, and,
the data storage device may include a digital data storage device.
The data storage device may include a portable data storage device.
The information security measure may be associated with the data
storage device, and may be associated with the computing device.
The input-selector may include an individual user. The recipient
may be an individual user. The input-selector and the recipient may
be a same party. The providing an output indicative of a result of
the automatic search may include providing a ranking for at least
two instances of the correlating occurrence-data in a hierarchy of
the found correlating occurrence-data.
[0020] The providing an output indicative of a result of the
automatic search may include providing a tentative
target-occurrence identifier. The method may include steps for
receiving another input-selection corresponding to the tentative
target-occurrence identifier, and providing an instance of
correlating occurrence-data in response to the another
input-selection.
[0021] The providing an output indicative of a result of the
automatic search may include providing a degraded representation of
an instance of the correlating occurrence-data. The method may
include steps for receiving another input-selection corresponding
to the degraded representation, and providing correlating
occurrence-data in response to the another input-selection.
[0022] The method may include receiving a redaction selection and a
tendered redaction authorization, and determining that at least one
of the redaction-selector and recipient possess a redaction right.
In response to the redaction selection and a determination that at
least one of the redaction selector and the recipient possess a
redaction right, redacting an instance of the plurality of
instances of occurrence-data from the stored occurrence-data. The
redacted instance of occurrence-data may correlate to the
target-occurrence representative feature. The redacted instance of
occurrence-data may not correlate to the target-occurrence
representative feature. The method may include, if occurrence-data
correlating to the target-occurrence representative feature is
found, and if at least one of the input-selector and recipient
posses an access right, provide the correlating occurrence-data to
the recipient.
[0023] Another embodiment provides an occurrence-data retrieval
system. The system includes a computing device operable to
communicate with a data storage device. The data storage device is
operable to store a plurality of instances of occurrence-data from
remote data storages, each instance of occurrence-data having a
representative feature sensed respectively by a device associated
with the remote data storage. The system also includes an
information security measure that protects instances of
occurrence-data stored in the data storage device from unauthorized
access, and instructions, which when implemented in a computing
device, cause the computing device to perform steps. The steps
include receive from a redaction-selector a redaction selection
corresponding to a target-occurrence having a representative
feature, and a tender of a redaction authorization. In response to
the tendered redaction authorization, determine if the
redaction-selector possesses a redaction right, and automatically
select a pattern recognition criteria corresponding to the
representative feature of the target-occurrence. In response to the
redaction selection corresponding to the target-occurrence,
automatically search the plurality of instances of occurrence-data
stored in the data storage device for data correlating to the
target-occurrence using the selected pattern recognition criteria.
If the redaction-selector possesses a redaction right, redact an
instance of the plurality of instances of occurrence-data from the
data storage device. The redacted instance of occurrence-data may
correlate to the target-occurrence representative feature. The
redacted instance of occurrence-data may not correlate to the
target-occurrence representative feature.
[0024] A further embodiment provides a method. The method includes
inputting a selection to a computing device corresponding to a
target-occurrence having a representative feature, a recipient
selection, and a tendered access authorization. The method includes
inputting a selection to the computing device corresponding to a
plurality of instances of stored occurrence-data protected by an
information security measure. Each instance of occurrence-data
originates from remote data storages, includes a representative
feature sensed respectively by a device associated with the remote
data storage, and respectively correlates to an occurrence. If the
tendered access authorization establishes an access right,
receiving an output indicative of a search of the plurality of
instances of stored occurrence-data for data correlating to the
target-occurrence. The data correlating to the target-occurrence
may be determined by a pattern recognition criteria automatically
selected in response to the target-occurrence. The plurality of
instances of occurrence-data may be stored in a data storage device
local to the computing device. The method may include inputting a
redaction selection and tendering a redaction authorization, and
determining if a valid redaction right is owned. If the tendered
access authorization establishes a valid redaction right is owned,
redacting an instance of the plurality of instances of
occurrence-data from the stored occurrence-data. The instance of
occurrence-data may correlate to the target-occurrence
representative feature. The redacted instance of occurrence-data
may not correlate to the target-occurrence representative
feature.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Aspects of the invention, together with features and
advantages thereof, may be understood by making reference to the
following description taken in conjunction with the accompanying
drawings, in the several figures of which like referenced numerals
identify like elements, and wherein:
[0026] FIG. 1 illustrates a sensor node, or "mote";
[0027] FIG. 2 illustrates a graph of a hypothetical data related to
a sensed parameter that may define an occurrence;
[0028] FIG. 3 is a table illustrating several classes of
occurrences, a relationship between an individual occurrence and at
least one characteristic or attribute of the individual
occurrences, and representative features of the individual
characteristics;
[0029] FIG. 4 illustrates a distributed sensor network;
[0030] FIGS. 5A and 5B include a flow diagram illustrating an
exemplary process in which sensor data correlating to a
target-occurrence is acquired from a sensor network and stored;
[0031] FIG. 6 illustrates a distributed sensor node occurrence-data
archival and retrieval system;
[0032] FIG. 7 is a flow diagram illustrating an exemplary process
that aggregates and stores a plurality of instances of correlated
sensor data in an occurrence-data archive;
[0033] FIG. 8 is a flow diagram that illustrates exemplary steps of
a process that searches and retrieves certain instances of stored
correlated sensor data from an occurrence-data archive;
[0034] FIG. 9 is a flow diagram illustrating exemplary steps of a
process that searches a plurality of instances of occurrence data
stored in a data vault or data lock box and provides an output;
[0035] FIG. 10 is a flow diagram illustrating exemplary steps of a
process providing the output of FIG. 9; and
[0036] FIG. 11 is a flow diagram illustrating exemplary steps of a
process that redacts a selected instance of occurrence data from
the plurality of instances of stored occurrence data described in
conjunction with FIG. 9.
DETAILED DESCRIPTION
[0037] In the following detailed description of exemplary
embodiments, reference is made to the accompanying drawings, which
form a part hereof. The detailed description and the drawings
illustrate specific exemplary embodiments by which the invention
may be practiced. Other embodiments may be utilized, and other
changes may be made, without departing from the spirit or scope of
the present invention;
[0038] Throughout the specification and claims, the following terms
take the meanings explicitly associated herein unless the context
dictates otherwise. The meaning of "a", "an", and "the" include
plural references. The meaning of "in" includes "in" and "on".
[0039] FIG. 1 illustrates a sensor node 20, or "mote," many of
which can be combined to form a sensor network. The sensor node 20
may be of various sizes, and may be as small as a quarter coin, or
smaller, as sensor node sizes are now in the millimeter range. The
sensor node 20 includes a power source 22, a logic
circuit/microprocessor 24, a storage device 25, a transmitter (or
transceiver) 26, a communications coupler 28 coupled to the
transmitter 26, and a sensor element 30. Alternatively, the mote
may be unpowered or passive, drawing its power from a reader or
another source.
[0040] In the illustrated embodiment, the power source 22 provides
power to the sensor node 20. For example, the power source 22 may
include a battery, a solar-powered cell, and/or a continuous power
supply furnished by an external power source, such as by connection
to a power line. By way of example, the storage device 25 includes
any computer readable media, such as volatile and/or nonvolatile
media, removable and/or non-removable media, for storing computer
data in permanent or semi-permanent form, and can be implemented
with any data storage technology. Alternatively, the storage device
25 may store data in a form that can be sampled or otherwise
converted into a form storable in a computer readable media.
[0041] The transmitter 26 transmits a data signal. In an optional
embodiment, the transmitter 26 both receives and transmits data
signals (transceiver). A "data signal" includes, for example and
without limitation, a current signal, voltage signal, magnetic
signal, or optical signal in a format capable of being stored,
transferred, combined, compared, or otherwise manipulated. The
transmitter 26 may include wireless, wired, infrared, optical,
and/or other communications techniques, for communication with a
central computing device or central station, and optionally other
sensor nodes, using the communications coupler 28. The
communications coupler 28 may include an antenna for wireless
communication, a connection for wired connection, and/or an optical
port for optical communication.
[0042] The sensor node 20 may include any type of data processing
capacity, such a hardware logic circuit, for example an application
specific integrated circuit (ASIC) and a programmable logic, or
such as a computing device, for example, a microcomputer or
microcontroller that include a programmable microprocessor. The
embodiment of the sensor node 20 illustrated in FIG. 1 includes
data-processing capacity provided by the microprocessor 24. The
microprocessor 24 may include memory, processing, interface
resources, controllers, and counters. The microprocessor 24 also
generally includes one or more programs stored in memory to operate
the sensor node 20. If an embodiment uses a hardware logic circuit,
the logic circuit generally includes a logical structure that
operates the sensor node 20.
[0043] The sensor node 20 includes one or more sensor elements 30
that are capable of detecting a parameter of an environment in
which the sensor node is located and outputting a data signal. The
sensor element 30 may detect at least one parameter from a group of
optical, acoustic, pressure, temperature, thermal, acceleration,
magnetic, biological, chemical, and motion parameters. The optical
parameter may include at least one from a group consisting of
infrared, visible, and ultraviolet light parameters. For example
and without limitation, the sensor element 30 may include a photo
sensor to detect a level or change in level of light, a temperature
sensor to detect temperature, an audio sensor to detect sound,
and/or a motion sensor to detect movement. The sensor element 30
may include a digital image capture device, such as for example and
without limitation, a CCD or CMOS imager that captures data related
to infrared, visible, and/or ultraviolet light images.
[0044] Typically, the sensor node 20 automatically acquires data
related to a parameter of the sensor node environment, and
transmits data to a central computing device. For example, the
sensor element 30 in a form of an acoustic sensor may acquire sound
levels and frequencies, and transmit the data related to the levels
and frequencies along with a time track using the transmitter 26
and the communication coupler 28. The acquisition may be on any
basis, such as continuously, intermittently, sporadically,
occasionally, and upon request. In an alternative embodiment, the
time track may be provided elsewhere, such as a device that
receives the sensor data.
[0045] By way of further example and without limitation, the sensor
element 30 in a form of an optical digital camera may periodically
acquire visual images, such as for example, once each second, and
to transmit the data related to visual images along with a time
track. In another example, the sensor element 30 in the form of a
temperature sensor may detect temperature changes in two-degree
temperature intervals, and to transmit each two-degree temperature
change along with the time it occurred. Each of the above examples
illustrates a sequence, ranging from continuous for acoustical
detection to a per occurrence basis for two-degree temperature
changes.
[0046] The sensor element 30 may sense operational parameters of
the sensor node 20 itself, such as its battery/power level, or its
radio signal strength. Sensor data, including a data related to a
sensed parameter, is transmitted from the sensor node 20 in any
signal form via the transmitter 26 and the communications coupler
28, to a receiver. The receiver may be, for example, another sensor
node 20, a central computing device, or any other data receiver.
The sensor data may include a time and/or date that the data
related to a parameter was acquired.
[0047] The sensor node 20 may include a unique identifier, and is
operable to communicate the identifier in an association with its
sensed parameter. In an alternative embodiment, the sensor node 20
may include a configuration that determines its location, for
example, by a GPS system, by triangulation relative to a known
point, or by communication with other sensor nodes. Alternatively,
the location of the sensor node 20 may be a known parameter
established previously. Similarly, location identification may be
associated with data originated and/or forwarded by the sensor
node.
[0048] FIG. 2 illustrates a graph 50 of a hypothetical
chronological sequence 52 of a sensed parameter that may define an
occurrence. The sequence 52 illustrates a chronological sequence of
a parameter that might be outputted by a sensor node, and is
plotted on the graph 50 with time on a x-axis and amplitude on a
y-axis. The sinusoidal sequence 52 includes several representative
features. A first representative feature is that the sequence 52
includes only two frequencies, A and B. A second representative
feature is that each frequency lasts for three cycles before the
sequence 52 changes to the other frequency. A third representative
feature is that the sequence 52 amplitude is generally the same
over the time T.
[0049] For example, assume that an individual user is seeking data
representative of a car accident. The car accident is the
target-occurrence. Further, assume that a characteristic of a car
accident is that an emergency vehicle may approach and/or be
present at the scene with its siren activated. Further, assume that
it is known that a "do-dah, do-dah, do-dah" type siren used by some
emergency vehicles, such as fire, ambulance, or police, generates
sound or acoustic waves that include the three features of the
sequence 52. Also, assume that the sequence 52 represents a
chronological sequence output parameter by an acoustic sensor, such
as element 30 of the sensor node 20 of FIG. 1. Application of a
pattern recognition criteria that recognizes the three above
representative features of a sensor data that includes the sequence
52 is likely to locate sensor data representative of the car
accident occurrence that involved a presence of siren. The sensor
data may be either from a single sensor node 20 or a plurality of
sensor nodes 20.
[0050] By way of further example, if the occurrence of interest is
passage of an emergency vehicle siren through an intersection
monitored by an acoustic sensor, a fourth representative feature
would be a Doppler shift in the frequencies A and B on the passage
of the vehicle. Expansion of the pattern recognition criteria to
include recognition of the fourth feature is likely to locate
sensor data representative of the passage of the emergency vehicle.
This example may be expanded where each intersection in a portion
of a city is individually monitored by networked, distributed
acoustic sensor nodes. Application of the expanded pattern
recognition criteria to the chronological sequences of acoustic
data outputted by the sensor nodes is expected to locate data
representative of the passage of the emergency vehicle through each
intersection, including a time of passage. Note that in this
example, the siren is a selected target-occurrence while in the
above example, the siren is a characteristic of the selected
target-occurrence, the car accident.
[0051] An occurrence includes anything that may be of interest, for
example, to a user, a computing device, or machine. An occurrence
may be or include, for example, a reference, an incident, an
accident, an event, a real world event, a change in a data
sequence, and a change in a time domain. An occurrence may be a
high-level matter such as a car crash or a riot, or a lesser-level
matter, such as a siren or gun shot. This detailed description uses
certain events having a sequence of at least one parameter that may
be detected by a sensor element to describe embodiments. However,
the invention is not so limited.
[0052] FIG. 3 is a table illustrating several classes of
occurrences, a relationship between an individual occurrence and at
least one characteristic or attribute of the individual
occurrences, and representative features of the individual
characteristics. Table of FIG. 3 illustrates an anticipated
relationship between occurrences, characteristics, and
features.
[0053] For example, occurrence 1 of FIG. 3 is a car crash. A car
crash includes a plurality of characteristics or attributes, such
as (a) breaking glass, (b) impact noise, (c) tire screech, and (d)
approach and presence of emergency vehicles. Each of these
characteristics has representative features that can be sensed by
one or more sensor nodes, such as the sensor node 20.
Characteristic or attribute (a), breaking glass of occurrence 1, a
car crash, is expected to include a representative feature of
sequential, high, and broadly-distributed sound frequencies that
would be sensed by an acoustic sensor, such as the sensor element
30 of FIG. 1. Characteristic (d), approach and presence of
emergency vehicles, is expected to include a representative feature
of a siren being sounded as an emergency vehicle approaches a car
accident scene. A more detailed example of representative features
of a "do-dah, do-dah" siren pattern is described in conjunction
with FIG. 2 above. Other types of emergency sirens are expected to
have different representative features.
[0054] By way of further example, a siren sound, which is a
characteristic of occurrence 1, may also be considered an
occurrence, and is shown as occurrence 2 of FIG. 3. FIG. 3 also
includes examples of fire, armored convey passage, and physical
assault as high-level occurrences, and a gun shot as a lesser-level
occurrence.
[0055] As described above, each occurrence has certain known and/or
discoverable features or representative features. In FIG. 2, the
graph 50 of the hypothetical chronological sequence 52 of a sensed
parameter illustrates three representative features that may
correspond to an occurrence.
[0056] One or more representative features are selected for
recognition of sensor data representative of an occurrence of
interest, which is also referred to as a target-occurrence.
Representative features are features that correspond to a
characteristic of an occurrence and provide a data representation
of the occurrence. A representative feature may be individually
selected by an input-selector, or automatically selected. Any
suitable pattern recognition criteria, such as which may be
expressed in an algorithm, method and/or device, is used to
identify one or more of the selected representative features of a
target-occurrence for identification, location, retention, and/or
retrieval of sensor data corresponding to the target-occurrence. In
certain embodiments, the pattern recognition criteria are computer
implemented. "Pattern recognition criteria" as used in this
specification may include anything that recognizes, identifies, or
establishes a correspondence with, one or more representative
features of an occurrence. While the fields of pattern recognition
and artificial intelligence are sometimes considered as separate
fields, or that one is a subfield of the other, pattern recognition
as used herein may include methods and/or devices sometimes
described as artificial intelligence. Further, pattern recognition
may include data or image processing and vision using fuzzy logic,
artificial neural networks, genetic algorithms, rough sets, and
wavelets. Further, a determination of which features are
representative features of a target-occurrence may also be
determined using pattern recognition.
[0057] FIG. 4 illustrates a distributed sensor network 70 that
includes an array of sensor nodes 80, a central computing device
90, at least one digital storage device, illustrated as a digital
storage device 100, and a plurality of communications links. The
sensor nodes of the plurality of sensor nodes 80 are similar to the
sensor node 20 of FIG. 1. For purposes of illustration, the sensor
nodes are given reference numbers indicative of their
communications tier with respect to the central computing device
90. The first tier has reference numbers 82.1.1-82.1.N, and the
second tier has reference numbers 82.2.1-82.2.N. Additional tiers
are not numbered for clarity. Each sensor node in the array of
sensor nodes 80 may sense a same parameter. Alternatively, a
plurality sensor nodes of the array of sensor nodes 80 may
respectively sense different parameters. For example, the sensor
node 82.1.1 may respectively sense acoustical pressure and sensor
node 82.1.2 may respectively sense temperature. The respective
parameters sensed by the individual sensor nodes may be mixed and
matched in any manner to provide a desired parameter description of
the area in which the array of sensor nodes 80 are deployed.
[0058] In an embodiment, the individual sensor nodes of the
plurality of sensor nodes 80 of the sensor network 70 are typically
distributed, that is they are physically separated from each other.
However, in certain embodiments, sensor nodes that sense different
parameters are grouped in proximity to provide a more complete data
related to a location. Further, in an embodiment, the sensor nodes
of the array of sensor nodes 80 are distributed over a geographical
area. Such distributed sensors may include sensing "real world"
environmental parameters occurring in a locale of each sensor, for
example and without limitation, weather, car crashes, and gunshots.
In another embodiment, the sensor nodes of the array of sensor
nodes 80 are distributed in a manner to sense a parameter related
to a physical entity, such as, for example and without limitation,
individual pieces of a distributed equipment, such as traffic
lights or cell-phone transmission towers, or a locale, such as
seats in a stadium.
[0059] An exemplary system implementing an embodiment includes a
computing device, illustrated in FIG. 4 as a central computing
device 90. In its most basic configuration, the computing device 90
typically includes at least one central processing unit, storage,
memory, and at least some form of computer-readable media. Computer
readable media can be any available media that can be accessed by
the computing device 90. By way of example, and not limitation,
computer-readable media might comprise computer storage media and
communication media.
[0060] Computer storage media includes volatile and nonvolatile,
removable and non-removable media implemented in any method or
technology for storage of data such as computer readable
instructions, data structures, program modules or other data.
Computer storage media includes, but is not limited to, RAM, ROM,
EPROM, flash memory or other memory technology, CD-ROM, digital
versatile disks (DVD) or other optical storage, magnetic cassettes,
magnetic tape, magnetic disk storage or other magnetic storage
devices, or any other medium that can be used to store the desired
data and that can be accessed by the computing system 90. The
computer storage media may be contained within a case or housing of
the computing device 90, or may be external thereto.
[0061] Communication media typically embodies computer-readable
instructions, data structures, program modules or other data in a
modulated data signal such as a carrier wave or other transport
mechanism and includes any information and/or delivery media. The
term "modulated data signal" means a signal that has one or more of
its characteristics set or changed in such a manner as to encode
information in the signal. By way of example, and not limitation,
communication media includes wired media such as a wired network or
direct-wired connection, and wireless media such as acoustic, radio
frequency, infrared, and other wireless media. Combinations of any
of the above should also be included within the scope of
computer-readable media. Computer-readable media may also be
referred to as computer program product.
[0062] The digital storage device 100 may be any form of a computer
data digital storage device that includes a computer storage media,
including the forms of computer storage media described above. The
digital storage device 100 may be a local digital storage device
contained within a case housing the computing device 90.
Alternatively, the digital storage device 100 may be a local and
external digital storage device proximate to the computing device
90, or remote to the computing device, and that coupled to the
computing device 90 in either case by a communications link 99.
[0063] The computing device 90 also includes communications ports
that allow the computing device to communicate with other devices.
More specifically, the computing device 90 includes a port 97 for a
wired communication link, such as the wired communication link 102
providing communications with at least one sensor node of the array
of sensor nodes 80. The computing device 90 also includes a
wireless transceiver or receiver coupled with a communications
coupler, such as the antenna 96, for wireless communication over a
link, such as the wireless communication link 104. The wireless
communications link 104 provides wireless communications with at
least one sensor node of the array of sensors devices 80. The
wireless communication link 104 may include an acoustic, radio
frequency, infrared and/or other wireless communication link. The
computing device 90 further includes a port 98 for wired, wireless,
and/or optical communication over a communication link 108 with a
network, such as a local area network, wide area network, and
Internet. Such networking environments are commonplace in offices,
enterprise-wide computer networks, intranets and the Internet. The
communications link may include an acoustic, radio frequency,
infrared and other wireless connection.
[0064] The computing device 90 may also have input device(s) 94,
such as keyboard, mouse, pen, voice input device, touch input
device, etc. The computing device 90 may further have output
device(s) 92, such as a display, speakers, printer, etc. may also
be included. Additionally, the computing device 90 may also have
additional features and/or functionality.
[0065] The computing device 90 may be implemented in any suitable
physical form, including a mainframe computer, a desktop personal
computer, a laptop personal computer, and a reduced-profile
portable computing device, such as a PDA or other handheld
device.
[0066] Logical operation of certain embodiments may be implemented
as a sequence of computer implemented steps, instructions, or
program modules running on a computing system and/or as
interconnected machine logic circuits or circuit modules within the
computing system.
[0067] The implementation is a matter of choice dependent on the
performance requirements of the computing system implementing and
embodiment. In light of this disclosure, it will be recognized by
one skilled in the art that the functions and operation of various
embodiments disclosed may be implemented in software, in firmware,
in special purpose digital logic, or any combination thereof
without deviating from the spirit or scope of the present
invention.
[0068] FIGS. 5A and 5B include a flow diagram illustrating an
exemplary process 120 in which sensor data correlating to a
target-event is acquired from a sensor network and stored. In
certain embodiments, the process 120 is implemented in a central
computer, such as the computing device 90 of FIG. 4. In other
embodiments, at least a portion of the process 120 is implemented
in a sensor node of an array of sensor nodes, such as the sensor
node array 80 of FIG. 4.
[0069] After a start block, the process 120 moves to block 122. At
block, 122, a computing device, such as the central computing
device 90, continuously receives sensed data of at least one
parameter from a sensor node over a communications link. The sensor
node may be any sensor node, such as the sensor node 82.1.2,
82.1.N, or 82.2.2 of the array of sensor nodes 80 of FIG. 4. The
communications link may be any communications link known in the
art, for example and without limitation, an optical, a wireless,
and/or a wired link. For example, FIG. 4 illustrates the sensor
node 82.1.2 communicating over the wired communications link 102,
and the sensor node 82.1.N communicating over the wireless
communications link 104. FIG. 4 also illustrates the sensor node
82.2.2 communicating over a wireless link 106 with the sensor node
82.1.3, which then relays and communicates the data from the sensor
node 82.2.2 with the computing device 90 over the wired
communications link 102.
[0070] Optimally, the sensed data is transmitted at intervals and
aggregated into the data related to the sensed at least one
parameter by a receiving device. In an alternative embodiment, the
sensed data may be transmitted continuously by the sensor node.
Furthermore, in another embodiment, the sensed data may include
continuously sampled data at a predetermined sampling rate, such as
a temperature reading captured during the first minute of every
five-minute interval, or such as a digital image captured once each
second.
[0071] At block 124, the received sensed data is continuously
stored in a storage device, such as the storage device 100, as
first sensor data set. In an alternative embodiment, the first data
set includes a multi-element data structure from which elements of
the data related to the sensed at least one parameter can be
removed only in the same order in which they were inserted into the
data structure. In another alternative embodiment, the first data
set includes a multi-element data structure from which elements can
be removed based on factors other than order of insertion.
[0072] At block 126, an input selection is received from an
input-selector of a target-event having at least one representative
feature. In a certain embodiment, the input-selector includes a
user, who inputs the selection of the target-event using the user
input device 94 of FIG. 4. The user may select the target-event
from a list of possible target-events displayed on the user output
device 92. The list for example, may be similar to the list of
occurrences of FIG. 3. In other embodiments, the input-selector
includes a machine, or a program running on a computing device,
such as the computing device 90.
[0073] In an embodiment, the input selection of the target-event
may include a selection an event that is directly of interest. For
example, a sound pattern of interest, such as the siren sound that
is event 2 of FIG. 3. In another embodiment, the input selection of
the target-event may be formulated in terms of a parameter that
correlates to the event that is directly of interest. For example,
where the event of interest is a fire, the input may be formulated
in terms of a siren sound indicating an approach or presence of
emergency vehicles. The siren sound is characteristic (a) of a
fire, which is event 3 of FIG. 3.
[0074] In a further embodiment, the input selection of the
target-event is formulated in terms of weighing and/or comparing
several instances of a sensed data of at least one parameter from a
plurality of sensor nodes to determine which of the several
instances provide a good representation of the target-event. For
example, the input selection may request the best sensed data from
six sensor, such as the best sensed data from six sensors that
heard a gun shot during a time period.
[0075] At block 128, a pattern recognition criteria corresponding
to at least one representative feature of the target-event is
selected. In an embodiment, the method includes at least one
representative feature of each possible target-event. The process
automatically selects one or more pattern recognition criteria for
recognition of sensor data representative of or corresponding to
the target-event. In certain embodiments, the pattern recognition
criteria are included with the process 120, or available to the
process from another source. For example, pattern recognition
criteria may be associated locally with the computing device 90, or
available to it over a communications link, such as the
communications link 108. In a further embodiment, pattern
recognition criteria are provided to the computing device by the
input-selector in conjunction with the input of selection of the
target-event.
[0076] At block 132, in response to the input selection
corresponding to the target-event, the first sensor data set is
automatically searched for data correlating to the at least one
target-event representative feature using the selected pattern
recognition criteria.
[0077] In a certain embodiment, the received input selection of the
target-event further includes a selection of a representative
feature of the target-event. The inputted selection of a
target-event representative feature may be any feature that the
input-selector chooses for searching sensor data. For example, the
selected representative feature may include a time period and
acoustic frequency components. The acoustic frequency components
may include a selected frequency pattern, such as a recognized
word, set of words, breaking glass, dog bark, door opening, alarm,
threshold acoustic level, and voiceprint. The selected
representative feature may include a selected electromagnetic
pattern, such as a visible light, infrared light, ultraviolet
light, and radar. In this embodiment, at block 128, a pattern
recognition criteria is automatically selected by instructions in
response to the selected representative feature. Further, at block
132, the first sensor data set is automatically searched using the
pattern recognition criteria selected in response to the inputted
representative feature.
[0078] At decision block 134, a determination is made if sensor
data correlating to the at least one target-event representative
feature was found. If the sensor data is not found, the process
branches to block 138. If the sensor data is found, the process
branches to block 136. At block 136, the instructions cause the
computing device 90 to store the correlated sensor data in a
retained data storage. The retained data storage may be at any
location. For example and without limitation, the retained data
storage may be local to the central computing device 90, such its
removable or non-removable media; it may included in the digital
storage device 100; or it may be a remote digital storage device
associated with the computing device 90 over a communications link,
such as the communications link 108. In an embodiment, access to
the retained data storage is restricted to authorized users. After
storage of the correlated sensor data in a retained data storage,
the process moves to block 138.
[0079] In certain embodiments, in addition to storing the sensor
data correlating to at least one target-event representative
feature, the process includes storing a portion of the sensor data
that was sensed before the found target-event representative
feature. In other embodiments, the instructions include storing a
portion of the sensor data that was sensed after the found
target-event representative feature. In still other embodiments,
the instructions include storing a portion of the sensor data that
was sensed both before and after the found target-event
representative feature. These embodiments allow data occurring
before and/or after the representative features to be saved.
[0080] In another embodiment, the process includes assigning a
tentative event-identifier to the correlated sensor data. For
example, if the target-event is a fire, and if a search of the
first data set for data correlating to at least one fire event
representative feature finds correlating sensor data, the process
includes association of a tentative event-identifier, such as
"fire," with the correlated sensor data. The trial-event identifier
is associated with the stored correlated sensor data at block
136.
[0081] At block 138, the data related to the sensed at least one
parameter is continuously deleted from the first data set according
to a deletion sequence. In an embodiment, the deletion sequence
includes a substantially first-in, first-out order. In another
embodiment, the deletion sequence includes a factor other than
order of insertion into the data set.
[0082] At block 142, the process returns to block 132 to search
another portion of the continuously received sensed data. The
process continues while the continuous sensed data is received. The
instructions then move to the stop block.
[0083] An embodiment provides a computer implemented process for
searching the data related to the sensed at least one parameter
from the first data set and storing correlated sensor data for both
the target-event as described above and another target-event before
deletion of the data from the first data set. In an alternative
embodiment, another input selection is received corresponding to
another target-event having at least one representative event
feature. The input selection is received in a manner substantially
similar to block 126. In a manner substantially similar to block
128, another pattern recognition criteria is automatically selected
corresponding to at least one of the representative features of the
selected another target-event.
[0084] In a manner substantially similar to block 132, in response
to the input selection corresponding to the another target-event,
the first sensor data set is automatically searched for data
correlating to the at least one target-event feature of the another
target-event using the selected pattern recognition criteria. In a
manner substantially similar to decision block 134, if sensor data
correlating to the at least one target-event representative feature
of the another target-event is found, the correlated sensor data is
stored in the same retained data storage used to store
representative features of the first target-event, or another
retained data storage.
[0085] A further embodiment includes substantially simultaneously
storing correlated sensor data for the target-event from two sensor
nodes, each node generating separate data related to a same or a
different sensed parameter. In such an embodiment, two parallel
instances of sensed parameters are searched by the computing device
90 of FIG. 3 for data correlating to at least one representative
feature of the target-event. In a manner substantially similar to
block 122, data related to a sensed parameter from a second sensor
node of the plurality of distributed sensor nodes is continuously
stored into a second sensor data set.
[0086] In a manner substantially similar to block 132, in response
to the input selection corresponding to the target-event, the
second sensor data set is automatically searched for data
correlating to the at least one target-event representative feature
using the selected pattern recognition criteria. In a manner
substantially similar to decision block 134, if sensor data
correlating to the at least one target-event representative feature
of the target-event is found in the second data set, the second
correlated sensor data is stored. The storage location may be the
same retained data storage used to store representative features of
the first target-event, or another retained data storage.
[0087] Yet another embodiment provides a process that substantially
simultaneously stores correlated sensor data for a plurality of
target-events from a respective plurality of sensor nodes, each
node generating a separate data related to a same or a different
sensed parameter. The manner and method of scaling the computer
process 120 for the parallel and substantially simultaneous storing
of correlated sensor data may be done in any manner known to those
in the art.
[0088] Another embodiment includes using the computing power and
storage of a sensor node, such as the sensor node 20 of FIG. 1, to
run at least a portion of the process 120. In conjunction with
block 126 of FIG. 5A, the target even input selection may be
preloaded into the sensor node, or may be communicated to the
sensor node over a communications link. Similarly, in conjunction
with block 128, the pattern recognition criteria may also be
preloaded into the sensor node, or may be communicated to the
sensor node over a communications link. At block 136, the retained
data storage that stores the correlated sensor data may be local to
the sensor node, such as the digital storage 25 of FIG. 1. The
process 120 includes the sensor node transmitting at least a
portion of the stored correlated sensor data over a communications
link to a central computing device, such as the central computing
device 90 of FIG. 4. The process 120 may further include deleting
the stored sensor data after the data has been communicated to the
central computing device. In an alternative embodiment, the process
120 includes the sensor node transmitting the stored correlated
sensor data to the central computing device in response to a pull
by the central computing device. In another alternative embodiment,
the process 120 includes the sensor node pushing the stored
correlated sensor data to the central computing device.
[0089] Alternatively, at block 136, the retained data storage may
be the digital storage device 100 of the central computing device
90 of FIG. 4. The process 120 may include instructions that cause
the sensor node to transmit at least a portion of the found
correlated sensor data to the digital storage device 100 for an
initial storage.
[0090] An embodiment includes a communication media embodying the
process 120, which, when implemented in a computer, causes the
computer to perform a method. For example, in an embodiment where
the process 120 is implemented in a computing device, such as the
computing device 90 of FIG. 4, instructions embodying the process
are typically stored in a computer readable media, such as without
limitation the storage media and memory of the computing device,
and loaded into memory for use.
[0091] A further embodiment includes a method implementing the
steps of the computerized process 120, and a computer readable
carrier containing instructions which, when implemented in a
computer, cause the computer to perform the method of the computer
process 120.
[0092] An exemplary system employing certain embodiments described
above may be illustrated by a network system of distributed
acoustic sensor nodes placed on a plurality of city traffic lights.
While the illustrative system describes the networked system as
owned by the city maintaining the traffic lights, the exemplary
system may have any ownership, such as a private, public, and
governmental, and may be used for any purpose, such as private,
public, governmental, and military.
[0093] The exemplary system includes an orientation toward
gathering and storing acoustic event data for later identification
and retrieval. The individual nodes may use the power supplied to
the traffic light as their power source, or alternatively, use
long-life batteries or solar power. The individual nodes may
communicate with a central computing device by sending sensor data
over the power lines serving the traffic light, separate wire
communication links, or wireless communications links. An
event-data storage program embodying certain embodiments described
above is operating on the central computing device. Depending on
the city's need to accumulate sensor data and total digital data
storage space requirements, a digital storage device within the
central computing device case may be used, or at least one local
larger capacity device proximate to the central computing device
may be used.
[0094] In operation of the exemplary system, each sensor node
transmits data related to sensed acoustic data generated by their
acoustic sensor element to the central computing device. While the
sensed acoustic data may be transmitted continuously by each sensor
node, optimally in this embodiment to conserve bandwidth, the data
is temporarily stored in the sensor node and transmitted to the
central computing device in batches. A portion of sensed acoustic
data for each sensor node in the network, including an
identification of the originating sensor node, is received by the
event-data storage program operating on the central computing
device and stored in a data set queue in the associated digital
storage device. Optimally, the sensed acoustic data for each sensor
node is stored in a separate data set queue. This illustrative
system contemplates that two things occur before the sensed
acoustic data is received. First, the event-data storage program
receive at least one target-event input selection. Second, a
pattern recognition criteria corresponding to at least one of the
representative features of the target-event be selected. For this
exemplary system, the selected target-events are a gunshot, siren,
tire screech, and loud voices. The event-data storage program
automatically searches each sensor data set for senor data having
representative features correlating to a gunshot, siren, tire
screech, or loud voices using the selected pattern recognition
criteria. If sensor data correlating to a representative feature of
a gunshot, siren, tire screech, and loud voices is found, the
program stores the correlated sensor data in a retained data
storage. The retained data storage may have sufficient capacity to
archive correlated event-data for a predetermined time period, such
as a week, a month, a year, or multiple years.
[0095] Optimally, the program also associates and stores a
tentative event-identifier, such as gunshot, siren, tire screech,
or loud voices, with the correlated sensor data. The associated
tentative event-identifier will allow city officials to search the
correlated sensor data by identifying and event from gunshot,
siren, tire screech, or loud voices, and searching the retained
data storage by tentative identifiers instead of what may be a more
complicated search use pattern recognition criteria. After the
batch sensed acoustic data is searched, the program automatically
deletes the sensor acoustic data from the data set queue. The
deletion minimizes the amount of digital data storage necessary in
the system by saving only sensor data correlating to selected
target-events.
[0096] While the above exemplary system includes gathering and
storing event-data on a non-real-time basis for later retrieval, an
embodiment allows the system to perform real-time tentative
identification of one or more target-events and save correlating
sensor data. For example, sensor nodes having sufficient computing
capacity may be preloaded with one or more input target-event
selections. Each sensor node would automatically and in
substantially real-time search sensor data generated by its local
sensor element for sensor data correlating to the input
target-event selection. Instead of storing for later transmission,
the found correlating sensor data would be immediately transmitted
to the central computing device and be available for use. The data
transmission may include associated tentative event-identifiers. In
effect, the sensor nodes filter their acoustical data and only
provide sensor data to the central computing device that
corresponds the inputted target-event selection. The event-data
program may then store the found correlating sensor data, and
notify a user in substantially real-time of receipt of data having
the tentative target identifiers. The notification may be by a
display on a monitor screen coupled with the central computing
device. The user may then listen to the correlated sensor data and
take appropriate action, such as notifying police or fire.
[0097] Another embodiment includes a mobile central computing
device that a user takes into communication range with a network of
remote sensor nodes. A mobile computing device, such as a laptop
and a reduced-profile computing device, provide mobility to the
computing device 90. The mobility allows a user to take the central
computing device 90 into the field and within transmission range of
certain sensor nodes of a distributed network of remote sensor
nodes. The sensor nodes typically have acquired and stored a
plurality of sensor data sets, each sensor data set representing a
respective feature sensed by a sensor element of its respective
sensor node. A communication link, typically a wireless link, is
established between the computing device 90 and one or more of the
sensor nodes of the array of sensor nodes 80 of the network of
remote or distributed sensor nodes 70. The user inputs a selection
of sensor data sets to be transmitted from the certain sensor nodes
to the computing device 90. In response, a process running on the
computing device 90 communicates with the one or more sensor nodes,
extracts the sensor data sets, stores them, and provides a
confirmation to the user that the selected sensor data sets have
been received. The user typically will receive the confirmation and
move the computing device into communication proximity to other
sensor nodes of the array of sensor nodes 80. Typically, the stored
plurality of sensor data sets are deleted from the sensor nodes
after transmission to the computing device 90 to free-up
storage.
[0098] FIG. 6 illustrates a distributed sensor node event-data
archival and retrieval system 150. The system 150 includes a
plurality of distributed sensor networks, illustrated as first,
second, and third distributed sensor networks 70, 152, and 162
respectively. The distributed sensor network 70 is described in
conjunction with FIG. 4, and the sensor networks 152 and 162 are
substantially similar to the sensor network 70. Each distributed
sensor network includes an array of sensor nodes, illustrated as a
first, second, and third arrays 80, 154, and 164 respectively. Each
sensor network also includes at least one central computing device,
illustrated as first, second, and third central computing devices
90, 156, and 166 respectively, and includes a plurality of
communications links. The arrays of sensor nodes 154 and 164 are
substantially similar to the array of sensor nodes 80 described in
conjunction with FIG. 4. For clarity, only several sensor nodes and
their communications links are illustrated in the arrays 80, 154,
and 156 in FIG. 6.
[0099] The second and third central computing devices 156 and 166
are substantially similar to the first central computing device 90
of FIG. 4. The second and third digital data storage devices 158
and 168, and the associated communications links 159 and 169 that
communicate with those central computing devices are substantially
similar to the first digital data storage device 100 and the first
communications link 99, also as described in conjunction with FIG.
4.
[0100] The system 150 also includes an aggregating computing device
170 that is substantially similar to the central computing device
90 of FIG. 4. The words "central," "aggregating," "collecting," and
"archival" are used in this specification, including the claims, to
identify certain devices and to illustrate a possible network
hierarchy environment of one or more embodiments. These words do
not limit the nature or functionality of a device. The system 150
illustrates a possible network hierarchy where, in an embodiment, a
plurality of central computing devices, illustrated as the central
computing devices 90, 156, and 166, receive and store sensor node
data from a plurality of sensor node arrays, illustrated as the
sensor nodes of the arrays 80, 154, and 164 respectively. The
system 150 also illustrates a possible network hierarchy where, in
an embodiment, the aggregating computing device 170 receives and
stores, i.e., aggregates, sensor data acquired by a plurality of
central computing devices, illustrated in FIG. 6 as central
computing devices 90, 156, and 166. In another embodiment, the
computing device 170 may function as a central computing device
providing sensor data it received and stored to another aggregating
computing device (not illustrated).
[0101] The computing device 170 communicates with at least one
remote digital data storage device, such as storage devices 100,
158, and 168, through their associated computing devices 90, 156,
and 166, respectively, using one or more communications links. As
illustrated in FIG. 6, the aggregating computing device 170 also
includes communications ports that allow the computing device to
communicate with other devices. These communications ports are
substantially similar to the communications ports of the computing
device 90 of FIG. 4. More specifically, the computing device 170
includes a sensor communication port 177 for a wired communication
link, such as the wire communication link 189, providing
communications with the central computing device 156 and its
associated digital data storage device 158. The computing device
170 also includes a wireless transceiver or receiver coupled with a
communications coupler, such as an antenna 176, for wireless
communication over a communications link, such as a wireless
communication link 186. FIG. 6 illustrates the wireless
communication link 186 coupling the computing device 170 and the
computing device 166, and its associated digital data storage
device 168. The computing device 170 further includes a network
communications port 178 for wired, wireless, and/or optical
communication over a communication link, such as the network
communications link 188, for communication with a network, such as
a local area network, wide area network, and Internet. FIG. 6 also
illustrates a communications link 188 as network link between the
central computing device 90 and its associated digital storage
device 99. The communications link 188 may include an acoustic,
radio frequency, infrared and other wireless connection.
[0102] The system 150 also includes at least one digital storage
device as an event-data archive, illustrated as an archival digital
data storage device 190, which may be substantially similar to the
digital data storage device 100 of FIG. 4. The archival digital
storage device 190 may be a local digital data storage device
contained within a case housing the computing device 170.
Alternatively, the archival digital storage device 190 may be a
local and external digital data storage device proximate to the
computing device 170, or it may be remote to the computing device.
The archival digital data storage device 190 is coupled to the
computing device in any event by a communications link 179.
[0103] The aggregating computing device 170 may also have input
device(s) 174, such as keyboard, mouse, pen, voice input device,
touch input device, etc. The computing device 170 may further have
output device(s) 172, such as a display, speakers, printer, etc.
may also be included. Additionally, the computing device 170 may
also have additional features and/or functionality.
[0104] FIG. 7 is a flow diagram illustrating an exemplary process
200 that aggregates and stores a plurality of instances of
correlated sensor data in an event-data archive. After a start
block, the process 200 moves to block 202. At block, 202, a
plurality of central computing devices, such as the central
computing devices 90, 156, and 166, each transmit a plurality of
instances of correlated sensor data to an aggregating computing
device. The instances of correlated sensor data are typically
acquired by a sensor node operable to sense at least one parameter,
and each instance has been correlated to an event having at least
one representative feature. The instances may be stored in one or
more digital data storage devices, such as the storage devices 100,
158, and 168, associated with the central computing devices 90,
156, and 166, respectively. In an alternative embodiment, at least
one digital data storage device is remote to its associated
computing device. The remote digital data storage device may be
included in one or more sensor nodes.
[0105] In the embodiment illustrated in FIG. 6, the correlated
sensor data is accessed from the storage devices 100, 158, and 168
by their associated central computing devices 90, 156, and 166, and
transmitted over their associated communications links 108, 186,
and 189, to the aggregating computing device 170. In an embodiment,
each instance of the sensor data was acquired by at least one
sensor node of a plurality of distributed sensor nodes, and each
sensor node is part of a network of sensor nodes. Further, each
instance of correlated sensor data may include an associated
tentative event-identifier, which typically is generated and
associated when the instance of correlating sensor data was
found.
[0106] In an alternative embodiment (not illustrated), instances of
correlated sensor data re pulled from the digital data storage
devices in response to a request communicated to their respective
central computing devices by the aggregating computing device 170.
In another embodiment, instances of correlated sensor data are
transmitted or pushed from the digital data storage devices by
their associated central computing device to the aggregating
computing device 170.
[0107] At block 204, the plurality of instances of correlated
sensor data are received. At block 206, the plurality of instances
of correlated sensor data are stored in an aggregating digital data
storage device, such as the digital data storage device 190. The
aggregating digital data storage device may be referred to in this
specification as an event-data archive. In an alternative
embodiment, the plurality of instances of sensor data stored in the
event-data archive are protected by an information security
measure. Such a protected or secured stored data arrangement may be
referred to in this specification as a "data vault" or "data
lock-box."
[0108] The information security measure typically includes
providing at least one of maintaining information confidentiality,
maintaining information integrity, and limiting access to
authorized persons. The information security measure may be any
security measure known to those skilled in the art, and at a
selected level commensurate with the value of the information
contained in the instances of correlated sensor data and any loss
that might accrue from improper use, disclosure, or degradation.
The information security measure may be implemented in software,
hardware, infrastructure, networks, or any other appropriate
manner. In an embodiment, the information security measure may be
associated with the digital data storage device, the plurality of
instances of correlated sensor data, and/or a computing device
having a communication link with the digital data storage
device.
[0109] Next, at block 208 the process 200 waits for more event
data. If additional event data is received, the process moves to
block 204 and receives the additional event data. The process 200
then proceeds to the stop block. In an alternative embodiment, the
process 200 includes deleting at least a portion of the instances
of correlated sensor data from the digital data storage devices
100, 158, and 168 after the instances have been transmitted to the
aggregating computing device.
[0110] The process 200, when implemented in a computing device,
causes the computing device to perform certain steps. For example,
in an embodiment where the process 200 is implemented in a
computing device, such as the aggregating computing device 170 of
FIG. 6, the instructions are typically stored in a computer
readable media, such as the storage media and/or memory of the
computing device, and loaded into memory for use. In certain
embodiments, the process 200 aggregates instances of sensor data
correlating to an event from a plurality of remote digital data
storage devices, and stores those instances on a digital data
storage device associated with an aggregating computer as an
event-data archive, such as the archival digital data storage
device 190 of FIG. 6.
[0111] FIG. 8 is a flow diagram that illustrates exemplary steps of
a process 220 that searches and retrieves certain instances of
stored correlated sensor data from an event-data archive. After a
start block, the process 220 moves to block 222. At block 222, an
input selection is received from an input-selector corresponding to
a target-event having at least one representative feature. The
input-selector may include any entity, such as a machine, a
computing device, and a user.
[0112] The input selection optimally further includes the
input-selector tendering an access authorization, which is used to
determine if the input-selector is a trusted entity. The tendered
access authorization may be by any method or device required by a
security measure protecting the instances of stored sensor data
from unauthorized access, such as for example, a password, and
thumb print. For example, a trusted entity may be a user, machine,
or computing device, identified on a list of trusted parties. For
example, the list of trusted parties may include employees and/or
computing devices associated with the owner of the sensor network
system. The tendered access authorization may be the
input-selector's personal identification. Further, a trusted entity
may be a member of a certain class, such as uniformed law
enforcement officers, or computing devices maintained by agencies
that employ uniformed law enforcement officers. For example,
uniformed law enforcement officers may include members of the
Federal Bureau of Investigation, Alcohol Tobacco and Firearms,
state patrol, county sheriffs, and local police. Another example of
a trusted party class is a prosecuting attorney, a defense
attorney, and a judicial officer.
[0113] In a less preferred embodiment, the instances of stored
sensor data are not protected by a security measure, and the input
selection does not include tender of an access authorization.
[0114] At block 224, a decision operation determines if the
tendered access authorization establishes the input-selector is a
trusted entity and possesses an access right to the stored
correlated sensor data. If the input-selector is a trusted entity
and has an access right, the process branches to block 226. If the
input-selector does not posses an access right, the process
branches to the end block. If a security measure is not protecting
the instances of stored sensor data, then the decision block 224 is
not necessary and the process moves from decision block 222 to
block 226.
[0115] At block 226, a pattern recognition criteria is selected
corresponding to at least one representative feature of the
target-event. The criteria is selected in a manner substantially
similar to block 128 described in conjunction with FIGS. 5A and 5B,
including the alternative embodiments. At block 228, in response to
the input selection corresponding to the target-event, a plurality
of instances of stored sensor data are automatically searched for
data correlating to the target-event using the selected pattern
recognition criteria.
[0116] At decision block 232, a decision operation determines if
sensor data correlating to the at least one target-event
representative feature is found. If the sensor data correlating to
the target-event is not found, the process branches to block 236,
where a message equivalent to "no data found" is provided. If
sensor data correlating to the target-event is found, the process
branches to block 234.
[0117] At block 236, the found correlated sensor data is provided.
In an embodiment, the input-selector is the recipient of the
correlated sensor data. In another alternative embodiment, a third
party is the recipient of the correlated sensor data. The third
party may include a machine, a computing device, and a user. In a
further embodiment, the input-selector selects a third party
recipient of the correlated sensor data. In an alternative
embodiment, the process at block 222 further includes receiving an
access authorization of the third part tendered by the
input-selector, and the process at decision block 224 further
includes determining if the third party recipient possesses an
access right before providing the correlated sensor data to the
third party. The process 220 then moves to the end block.
[0118] In a further alternative embodiment of the process 220, the
search at block 228 proceeds in response to an input-selector
designation of a target tentative-event-identifier. In this
embodiment, the received plurality of instances of correlated
sensor data each include an associated tentative-event-identifier.
At block 222, the received target-event selection includes an input
selection corresponding to a target tentative event-identifier. If
a target tentative event-identifier is selected and no reason
exists to search for a representative feature, the block 226 may be
bypassed. At block 228, in response to the input selection
corresponding to the target tentative event-identifier, the
plurality of instances of sensor data are automatically
searched-for data correlating to the target tentative
event-identifier. If any event data is found correlating to the
target tentative event-identifier at decision block 232, the found
sensor data correlating to the target tentative event-identifier is
provided at block 234.
[0119] The process 220, when implemented in a computing device,
causes the computing device to perform steps. In certain
embodiments, the process 220 implements a process that searches and
retrieves instances of stored sensor data from an event-data
archive protected by a security measure, such as the archival digit
data storage device 190 coupled to the computing device 170 of FIG.
6. In other embodiments, the process 220 uses a local computing
device to search and retrieve instances of stored sensor data from
remote digital data storage devices, such as the digital data
storage device 168.
[0120] The process 220, when implemented in a computing device,
causes the computing device to perform certain steps. For example,
in an embodiment where the process 220 is implemented in a
computing device, such as the aggregating computing device 170 of
FIG. 6, the instructions are typically stored in a computer
readable media, such as the storage media and/or memory of the
computing device, and loaded into memory for use.
[0121] An exemplary system employing certain embodiments described
above may be illustrated by three network systems of distributed
sensors, and an aggregating computing device. Referring to FIG. 6,
the illustrative exemplary system includes the previously described
exemplary network system of distributed acoustic sensors placed on
city traffic lights as the first sensor network 70, an exemplary
network system of distributed digital image capture devices located
in city parking garages and lots as the second sensor network 152,
and an exemplary network of distributed heat/fire thermal sensors
located in city buildings as the third sensor network 162. Each
exemplary sensor network automatically stores correlated sensor
data in an associated retained data storage, such as the digital
data storage devices 100, 158, and 168.
[0122] As with FIG. 4, while the illustrative exemplary system
describes the networked system as owned by the city, the
illustrative exemplary system may have any ownership, such as a
private, public, and governmental, and may be used for any purpose,
such as private, public, governmental, and military. Further, the
sensor networks may have different owners. For example, the first
sensor network 70 may be owned by the city, the second sensor
network 152 may be privately owned by a parking garage operator,
and the third sensor network 162 may be privately owned by a fire
alarm company.
[0123] The illustrative exemplary system further includes an
aggregating computing device communications linked to the sensor
networks, such as the aggregating computing device 170 and its
archival digital data storage device 190. The central computing
devices of the three networks transmit the correlated sensor data
from their retained data storage to the aggregating computing
device. The aggregating computing device receives and stores the
correlated sensor data from the three networks in an event-data
archive on its associated digital data storage device, such as
device 190. The event-data archive includes a data structure
suitable for later search and retrieval. The event-data archive is
subject to an information security measure that protects the sensor
data stored in the event-data archive from unauthorized access. The
security measure is controlled by the aggregating computing device.
The central computing devices delete the correlated sensor data
from their associated retained data storage after transmission to
the aggregating computing device. This frees storage space for the
constant stream of additional correlated sensor data that is
continuously transmitted by sensor nodes of their respective sensor
networks.
[0124] A requesting entity may be an employee or official of an
owner or operator of one of the sensor networks, or may be a
potentially authorized person, machine, network or other entity. A
requesting entity desiring sensor data on an event, such as
shooting, enters a gunshot target-event selection on a user input
device of the aggregating computing device, and tenders an
identification number as an access authorization. In this example,
the gunshot (event 6 of FIG. 3) may have occurred near an
intersection controlled by a city traffic light at a known
date.
[0125] An event-data retrieval process operating on the aggregating
computing device receives the target-event selection and the
employee identification number. The process determines that the
requesting entity is a trusted entity and possesses an access
right. In response to the gunshot target-event selection, the
event-data retrieval process automatically selects a pattern
recognition criteria corresponding to at least one representative
feature of a gunshot. Then, the event-data retrieval process in
response to the gunshot input selection, automatically searches the
event-data archive for instances of acoustic sensor data
correlating to the at least one representative feature of a gunshot
on the known date. Correlating found instances of archived sensor
data are provided to the requesting entity, or a trusted third
party selected by the requesting entity.
[0126] In further reference to FIG. 8, another embodiment provides
a process that searches and retrieves certain instances of stored
correlated sensor data from an event-data archive. After a start
block, the embodiment includes receiving an input selection from an
input-selector, similar to the process 220 at block 222. The input
selection corresponds to a target-occurrence having a
representative feature. A filter corresponding to the
representative feature of the target-occurrence is selected. A
plurality of instances of occurrence data stored in a data set are
filtered for data correlating to the target-occurrence
representative feature a using the selected filter. Each instance
of the stored occurrence data has a representative feature. An
output responsive to the filtering is provided. The process then
ends. The filtering step may further include automatically
filtering the data stored in the data set. In a further embodiment,
the output responsive to the filtering correlates to a
target-occurrence representative feature, which is stored in
another data set. Alternatively, in another embodiment, the output
responsive to the filtering does not correlate to a
target-occurrence representative feature. The non-correlating
output is stored in anther data set.
[0127] FIG. 9 is a flow diagram illustrating exemplary steps of a
process 300 that searches a plurality of instances of event data
stored in a data vault or data lock box and provides an output.
Each instance of the event data has at least one representative
feature, is stored in a digital data storage device, and is
protected by an information security measure. The digital data
storage device may be a local digital data storage device or a
remote digital data storage device. The information security
measure may be associated with the digital data storage device, the
plurality of instances of stored event data, and/or a computing
device having a communication link with the digital data storage
device. In another embodiment, the digital data storage device
includes a portable digital data storage device, such as an
external hard drive, a DVD, a CD, a floppy disk, and a flash memory
device. In a further embodiment, the event data includes sensor
data generated by a plurality of networked sensor nodes.
[0128] The process 300 is similar to the process 220. After a start
block, the process 300 moves to block 302. At block 302, an input
selection is received from an input selector, the input selection
corresponding to a target-event having at least one representative
feature. The received input selection further includes an output
recipient selection and a tendered access authorization.
[0129] At block 304, in response to the tendered access
authorization, a decision operation determines if an access right
to the plurality of instances of stored event data protected by the
information security measure is possessed by at least one of the
input-selector and the recipient. If the decision operation
determines that either the input-selector and/or the recipient are
a trusted entity and posses an access right to the instances of
stored event data, the process branches to block 306. If neither
the input-selector nor the recipient is a trusted entity, the
process branches to the end block. In an alternative embodiment,
the input-selector and the recipient must each possess an access
right.
[0130] At block 306, a pattern recognition criteria is selected
corresponding to at least one representative feature of the target
event. The criteria is selected in a manner substantially similar
to block 128 described in conjunction with FIGS. 5A and 5B, and to
block 226 described in conjunction with FIG. 8, including the
alternative embodiments.
[0131] At block 308, in response to the input selection
corresponding to the target event, the plurality of instances of
stored event data are automatically searched for data correlating
to the at least one target-event representative feature using the
selected pattern recognition criteria.
[0132] At decision block 312, a decision operation determines if
event data correlating to the at least one target-event
representative feature was found. If the event data correlating to
the target-event representative feature was not found, the process
branches to block 316, where a message equivalent to "no data
found" is provided. If event data correlating to the target was
found, the process branches to block 314. At block 314, an output
indicative of the result of the automatic search at block 308 is
provided to the recipient.
[0133] In a further alternative embodiment of the process 300, the
search at block 308 proceeds in response to an input-selector
designation of a target tentative event-identifier in a
substantially similar manner as the process 200 described in
conjunction with FIG. 8.
[0134] The process 300, when implemented in a computing device,
causes the computing device to perform certain steps. For example,
in an embodiment where the process 300 is implemented in a
computing device, such as the aggregating computing device 170 of
FIG. 6, the instructions are typically stored in a computer
readable media, such as the storage media and/or memory of the
computing device, and loaded into memory for use.
[0135] FIG. 10 is a flow diagram illustrating exemplary steps of a
process 350 providing the output of the block 314 of FIG. 9. The
illustrated embodiment includes a set of possible outputs 360 from
the output at block 314. The set of possible outputs 360
illustrated in FIG. 10 includes a first subset of outputs for event
data correlating to the target-event representative feature, and a
second subset of outputs for event data not correlating to the
target-event representative feature, i.e., non-correlating. The
first subset includes a correlating tentative event-identifier 362,
a degraded correlating event-data representation 363, and a
correlating event data 364. The second subset includes a
non-correlating tentative event-identifier 366, a degraded
non-correlating event-data representation 367, and a
non-correlating event data 368. The process 350 at block 314
includes a default configuration, indicated by solid hierarchal
lines 361, that provides the correlating tentative event-identifier
362 and the non-correlating tentative event-identifier 366. In an
alternative embodiment, the output configuration provides the
degraded correlating event-data representation 363 and the degraded
non-correlating event-data representation 367. In another
alternative embodiment, the output configuration provides only the
correlating event data 364.
[0136] At block 314, the initial output is provided to the
input-selector and/or recipient in any manner and using any output
device, such as being displayed on a monitor of a computing device.
For example, the output may include displaying a table having
columns that include an event data date, a tentative event
identifier, and a correlating/non-correlating status. Individual
instances of the plurality of instances of stored event data are
individually displayed in rows of the table. For example, in
response to a target-event selection of a gunshot, which is event 6
of FIG. 3, one row may display a date of May 17, 2004, a tentative
event-identifier of a "gunshot," and a status of "correlating."
Another row may display the same date of May 17, 2004, a tentative
event-identifier of "unknown" because no correlation to a
representative feature of a gunshot was found, and a status of
"non-correlating." In an alternative embodiment, the output at
block 314 may include a ranking for at least two instances of the
correlating event data in a hierarchy of the found correlating
event data. For example, if the provided output in the above
example includes a plurality of events having "gunshot" tentative
event-identifiers, the provided output may further include a
relative or absolute ranking based on the acoustic intensity of the
respective events as an aid to the recipient in evaluating the
event data.
[0137] At block 322, an event-data selection is received from the
input-selector, who may be the recipient. The selection corresponds
to at least one of the instances of event data provided by the
process at block 314 and requests provision of more detail related
to the provided instances. In the default configuration, the input
selection may correspond to a tentative event-identifier. For
example, the input selection may request provision of degraded
correlating event data corresponding to the event of May 17, 2004,
and tentatively identified as gunshot.
[0138] At block 324, the selected event data is provided in a form
of degraded correlating data. In an embodiment, the degraded
correlating event data includes sufficient data for the recipient
to make a preliminary determination whether the event appears to be
a gunshot. For example the recipient may listen to the degraded
data or view a display of a time-frequency analysis of the degraded
data. The process 350 then terminates at the end block.
[0139] If the recipient possesses an access authorization for the
correlating event data 364, the event-data selection may include
receiving another input selection that requests that the
correlating event-data be provided. The process at block 316
receives the another event-data selection, and at block 318
provides the output. Continuing with the above example, the
recipient may request complete event data (364) from all the
sensors that correlates to the gunshot.
[0140] The process 350, when implemented in a computing device,
causes the computing device to perform certain steps. For example,
in an embodiment where the process 350 is implemented in a
computing device, such as the aggregating computing device 170 of
FIG. 6, the instructions are typically stored in a computer
readable media, such as the storage media and/or memory of the
computing device, and loaded into memory for use.
[0141] FIG. 11 is a flow diagram illustrating exemplary steps of a
process 400 that redacts a selected instance of event data from the
plurality of instances of stored event data described in
conjunction with FIG. 9. After a start block, the process moves to
block 402, where a redaction selection and a tendered redaction
authorization are received. The redaction selection includes a
selection of at least one of the plurality of instances of event
data. In an embodiment, the redaction selection may be correlated
with the provided output at block 314 of FIGS. 9 and 10. Using the
above example where a plurality rows are displayed in a table on a
monitor, individual target-event-identifiers may be hyperlinked.
This allows an input-selector to select an event for redaction by
activating a link in a displayed row.
[0142] At block 404, in response to the tendered redaction
authorization, a decision operation determines if at least one of
the input-selector and the recipient possess a redaction right to
the plurality of instances of stored event data protected by the
information security measure. If the decision operation determines
that either the input-selector and/or the recipient are a trusted
entity and posses a redaction right, the process branches to block
406. If neither the input-selector nor the recipient is a trusted
entity, the process branches to the end block.
[0143] At block 406, the selected event data is redacted from the
plurality of instances of the stored event data. The redacted
instance of event data may or may not correlate to the at least one
target-event representative feature. The process 400 then
terminates at the end block.
[0144] The process 400, when implemented in a computing device,
causes the computing device to perform certain steps. For example,
in an embodiment where the process 400 is implemented in a
computing device, such as the aggregating computing device 170 of
FIG. 6, the instructions are typically stored in a computer
readable media, such as the storage media and/or memory of the
computing device, and loaded into memory for use.
[0145] An exemplary system employing certain embodiments described
in conjunction with FIGS. 9-11 may be illustrated using the
exemplary system of the three network systems of distributed
sensors and the aggregating computing device previously described
in conjunction with FIG. 8. Continuing with the previous
illustration, the event-data archive associated with the
aggregating computing device now contains correlating event data
acquired from the three-network system over time, such as a year.
The gunshot has resulted in litigation, and the litigants request
discovery of correlating event data in the city's data vault, which
is the city's event-data archive protected by a security measure.
The city is willing to provide relevant instances event data to the
litigants and a court, but unwilling to provide other instances of
event data based on proprietary and citizen privacy concerns.
[0146] A trusted person designated by the court and given an access
authorization by the city provides an input selection corresponding
to the gunshot event of May 17, 2004. For example, the trusted
person may be a neutral expert, an expert witness for a party, and
a magistrate. The input selection is received by an archival
event-data process described in conjunction with FIGS. 9-11, and a
determination made that the trusted person acting as an
input-selector possesses an access right to the data vault. In
response to the gunshot target-event selection, the archival
event-data retrieval process automatically selects a pattern
recognition criteria corresponding to at least one representative
feature of a gunshot. The archival event-data retrieval process, in
response to the gunshot input selection, automatically searches the
event-data archive for instances of acoustic sensor data
correlating to the at least one representative feature of a gunshot
on the known date.
[0147] An initial output indicative of the search result is
provided to the trusted person. In the exemplary embodiment, the
default output configuration described above provides a table
displaying the correlating tentative gunshot-identifiers (362) and
the non-correlating tentative gunshot-identifiers (366) in rows.
The trusted person provides an event-data selection that
corresponds to at least one of the instances of tentative
gunshot-identifiers initially provided by the process. For example,
an initial output may indicate that a plurality of sensors
generated acoustical data correlating to at least one
representative feature of a gunshot, and the input selector selects
three of these instances. The event-data selection is received from
the input-selector, and the archival event-data retrieval process
provides the trusted person with the three selected instances of
degraded correlating event data corresponding to the gunshot. The
trusted person listens to the three instances of degraded event
data. If the trusted person concludes two of the three instances of
event data relate to the gunshot, the trusted person then requests
and is provided with the two complete event data for the two
instances.
[0148] Another embodiment of the exemplary archival event-data
process provides a redaction whereby the city through a
representative, or the trusted person, may remove certain instances
of event data from the plurality of instances of event data in the
city's data vault. The redacted data vault may then be given to a
third party much like a redacted paper document. Preferably, the
city retains a duplicate of their data vault prior to beginning the
redaction process. The process includes receiving the redaction
selection from the trusted party, and a tender of a redaction
authorization. For example, the redaction selection may be
formulated in terms of redacting all event data except for the
three selected instances of event data correlating to a gunshot.
Alternatively, the redaction selection may be inverted to redact
only the three selected instances of event data correlating to a
gunshot. Since redaction involves alteration of data from the data
vault, the city may require a separate redaction right in addition
to the access right.
[0149] The process determines that the trusted party possesses a
redaction right. In response to the redaction selection, all but
the three instances of event data are redacted from the data vault.
The data vault and the three selected instances of gunshot data
stored therein may be made accessible to others involved in the
litigation.
[0150] Although the present invention has been described in
considerable detail with reference to certain preferred
embodiments, other embodiments are possible. Therefore, the spirit
or scope of the appended claims should not be limited to the
description of the embodiments contained herein
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