Source: http://www.google.com/patents/US7457834?dq=5,815,794
Timestamp: 2017-05-28 03:13:46
Document Index: 401303249

Matched Legal Cases: ['§119', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10', 'application No. 10']

Patent US7457834 - Aggregation and retrieval of network sensor data - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsMethods, devices, and systems that aggregate and retrieve network sensor data. In one approach, an exemplary method includes receiving a plurality of instances of correlated sensor data from a plurality of remote data storages, each instance of sensor data respectively correlating to an occurrence. In...http://www.google.com/patents/US7457834?utm_source=gb-gplus-sharePatent US7457834 - Aggregation and retrieval of network sensor dataAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS7457834 B2Publication typeGrantApplication numberUS 10/903,692Publication dateNov 25, 2008Filing dateJul 30, 2004Priority dateJul 30, 2004Fee statusPaidAlso published asUS8271449, US20060026118, US20090119267Publication number10903692, 903692, US 7457834 B2, US 7457834B2, US-B2-7457834, US7457834 B2, US7457834B2InventorsEdward K. Y. Jung, Clarence T. TegreeneOriginal AssigneeSearete, LlcExport CitationBiBTeX, EndNote, RefManPatent Citations (67), Non-Patent Citations (99), Referenced by (38), Classifications (9), Legal Events (7) External Links: USPTO, USPTO Assignment, EspacenetAggregation and retrieval of network sensor data
US 7457834 B2Abstract
Images(13) Claims(104)
(a) a computing device operable to communicate with a local data storage device and with a remote data storage operable to store a plurality of instances of mote data, each instance of mote data correlating to a respective occurrence; and
(b) instructions, which when implemented in a computing device, cause the computing device to perform steps including:
(i) receive the plurality of instances of mote data from the remote storage device;
(ii) store the received plurality of instances of correlated mote data in the local data storage device;
(iii) receive an input selection corresponding to a target-occurrence having at least one representative feature;
(iv) automatically search the stored plurality of instances of mote data for mote data correlating to the target-occurrence; and
(v) if mote data correlating to the target-occurrence is found, provide the correlating mote data.
2. The system of claim 1, wherein the search for mote data correlating to the target-occurrence includes correlating to at least one representative feature of the occurrence.
3. The system of claim 1, wherein at least one occurrence includes a real world event.
4. The system of claim 1, wherein at least one occurrence includes a real world incident.
5. The system of claim 1, wherein at least one occurrence includes a change in a data sequence.
6. The system of claim 1, wherein at least one occurrence includes a change in a time domain.
7. The system of claim 1, wherein the plurality of instances of mote data include a first instance of mote data correlating to a respective first occurrence and a second instance of mote data correlating to a respective second occurrence.
8. The system of claim 7, wherein the first and second occurrences relate to substantially a common time.
9. The system of claim 7, wherein the first and second occurrences relate to substantially a same location.
10. The system of claim 7, wherein the first and second occurrences include an at least substantially similar representative feature.
11. The system of claim 7, wherein the mote data of the first occurrence relates a first matter and the mote data of the second occurrence relates to a second matter.
12. The system of claim 11, wherein the first and second matters are substantially similar.
13. The system of claim 1, wherein the representative feature is a frequency characteristic.
14. The system of claim 13, wherein the frequency characteristic is an acoustic frequency characteristic.
15. The system of claim 14, wherein the acoustic frequency characteristic includes an acoustic signature of a known event.
16. The system of claim 13, wherein the frequency characteristic is a light frequency characteristic.
17. The system of claim 1, wherein the representative feature is an amplitude characteristic.
18. The system of claim 1, wherein the representative feature includes a frequency characteristic and an amplitude characteristic.
19. The system of claim 1, wherein the representative feature is a magnetic characteristic.
20. The system of claim 1, wherein the representative feature includes a repetitive sequence.
21. The system of claim 20, wherein the repetitive sequence includes a chronological sequence.
22. An occurrence-data archival system, comprising:
(a) a computing device operable to communicate with a local data storage and a plurality of remote data storages; and
(b) instructions that cause a computing device to perform steps including:
(i) receive a plurality of instances of correlated mote data from the plurality of remote data storages, each instance of mote data respectively correlating to an occurrence; and
(ii) store the received plurality of instances of correlated mote data in the archival data storage;
wherein at least one instance of the received mote data includes an associated tentative occurrence-identifier; and
wherein the tentative occurrence-identifier is associated by a method including;
receiving an input selection corresponding to a target-occurrence having at least one representative feature;
automatically selecting a pattern recognition criteria corresponding to at least one representative feature of the target-occurrence;
search mote data for data correlating to the target-occurrence using the selected pattern recognition criteria; and
if mote data correlating to the target-occurrence representative feature is found, associating the tentative occurrence-identifier with the correlating mote data.
23. The system of claim 22, further including (c) a local data storage device.
24. The system of claim 23, wherein the local data storage device is operable as an archival data storage.
25. The system of claim 22, wherein the correlating to an occurrence includes correlating to at least one representative feature of the occurrence.
26. The system of claim 25, wherein the at least one representative feature includes a sequence.
27. The system of claim 22, wherein the occurrence includes a real world event.
28. The system of claim 22, wherein the occurrence includes a real world incident.
29. The system of claim 22, wherein the plurality of instances of mote data include a first instance of mote data correlating to a respective first occurrence and a second instance of mote data correlating to a respective second occurrence.
30. The system of claim 29, wherein the mote data of the first occurrence relates a first matter and the mote data of the second occurrence relates to a second matter.
31. The system of claim 30, wherein the first and second matters are substantially similar.
32. The system of claim 22, wherein each instance of mote data includes a respective sequence.
33. The system of claim 32, wherein the sequence includes a chronological sequence.
34. The system of claim 22, wherein each instance of mote data was acquired by at least one mote of a plurality of distributed motes.
35. The system of claim 34, wherein each mote is part of a network of motes.
36. The system of claim 22, wherein at least one instance of the mote data was acquired by a mote operable to sense at least one parameter.
37. The system of claim 22, further including (c) an information security measure operable to protect the plurality of instances of mote data stored in the local data storage.
38. The system of claim 37, wherein the information security measure protects by providing at least one item selected from a group consisting of information confidentiality, information integrity, and access control.
39. The system of claim 22, wherein the instructions further include (iii) delete a portion of the stored data from the at least one remote data storage.
40. A method implemented in a computing device, the method comprising:
(a) receiving a plurality of instances of correlated mote data from a plurality of remote data storages, each instance of mote data respectively correlating to an occurrence;
(b) storing the received plurality of instances of correlated mote data in a local storage device; and
(c) protecting the stored plurality of instances of correlated mote data with an information security measure;
wherein the information security measure protects the plurality of received instances of mote data by providing at least one selected from a group consisting of information confidentiality, information integrity, and access control.
41. The method of claim 40, wherein the receiving step further includes receiving the plurality of instances of mote data in response to a data push.
42. The method of claim 40, further comprising (d) requesting the plurality of instances of mote data from the remote data storage.
43. The method of claim 40, wherein the occurrence includes a real world event.
44. The method of claim 40, wherein the occurrence includes a real world incident.
45. The method of claim 40, wherein the occurrence includes a change in a data sequence.
46. The method of claim 40, wherein the occurrence includes a change in a time domain.
47. The method of claim 40, wherein the plurality of instances of mote data include a first instance of mote data correlating to a respective first occurrence and a second instance of mote data correlating to a respective second occurrence.
48. The method of claim 47, wherein the first and second occurrences happened at substantially a same time.
49. The method of claim 47, wherein the first and second occurrences happened at substantially a same location.
50. The method of claim 47, wherein the first and second occurrences include an at least substantially similar representative feature.
51. The method of claim 47, wherein the mote data of the first occurrence relates a first matter and the mote data of the second occurrence relates to a second matter.
52. The method of claim 51, wherein the first and second matters are substantially similar.
53. The method of claim 47, wherein each instance of mote data includes a respective sequence.
54. The method of claim 53, wherein the sequence includes a Chronological sequence.
55. The method of claim 47, wherein each occurrence includes a sequence and representative feature.
56. The method of claim 47, wherein each instance of mote data includes an associated tentative occurrence-identifier.
57. The method of claim 47, further comprising (d) deleting at least one of the plurality of instances of mote data from its respective remote data storage.
58. A communication storage media containing computer instructions, which, run on a computing device, cause the computing device to perform the method comprising:
(b) storing the received plurality of instances of correlated mote data in a local data storage device; and
wherein the plurality of instances of mote data include a first instance of mote data correlating to a respective first occurrence and a second instance of mote data correlating to a respective second occurrence.
(a) moving a portable central computing device to location such that the portable central computing device communicates with at least one mote of a network of distributed motes, each mote operable to sense and store a plurality of data sets each respectively representing a respective feature, each respective feature having a correlation to an occurrence; and
(b) causing the central computing device to acquire at least one mote data set;
wherein each mote further includes operability to filter the plurality of data sets for a feature correlating to a target-occurrence; and
wherein causing the central computing device to acquire at least one data set further includes causing the central computing device to acquire at least one data set having a feature correlating to the target occurrence.
60. The method of claim 59, further including (c) selecting through the central computing device a mote data set to be transmitted from the at least one mote to the central computing device.
61. The method of claim 60, further including (d) confirming that the at least one selected mote data set is acquired by the central computing device.
62. An occurrence-data retrieval system, comprising:
(a) a computing device operable to communicate with a data storage operable to store a plurality of instances of mote data acquired by at least one mote of a mote network, each instance of mote data correlating to a respective occurrence; and
(i) receive from an input-selector an input selection corresponding to a target-occurrence;
(ii) automatically search the plurality of instances of mote data stored in the data storage for mote data correlating to the target-occurrence; and
(iii) if mote data correlating to the target-occurrence is found, provide the correlating mote data.
63. The system of claim 62, wherein each occurrence includes a representative feature.
64. The system of claim 62, wherein the data storage is remote to the computing device.
65. The system of claim 64, wherein the computing device communicates with at least one remote data storage through an intermediate computing device.
66. The system of claim 62, wherein the data storage is local to the computing device.
67. The system of claim 62, wherein the provide instruction further includes provide the correlating mote data to the input-selector.
68. The system of claim 62, wherein the provide instruction further includes provide the correlating mote data to a third party.
69. The system of claim 62, wherein the instructions further include:
(iv) receive the plurality of instances of mote data from at least one remote data storage; and (v) store the received plurality of instances of correlated mote data in a local data storage. 70. An occurrence-data retrieval system, comprising:
(a) a local digital data storage device operable to store a plurality of instances of mote data acquired from a plurality of motes of a network of distributed motes, each instance of mote data correlating to at least one representative feature of an occurrence;
(b) a central computing device operable to communicate with the local digital data storage device; and
(c) instructions that cause the computing device to perform steps, including:
(i) receive from an input-selector an input selection corresponding to a target-occurrence having at least one representative feature;
(ii) automatically select a pattern recognition criteria corresponding to at least one representative feature of the target-occurrence;
(iii) in response to the input selection corresponding to the target-occurrence, automatically search the plurality of instances of stored mote data for data correlating to the target-occurrence using the selected pattern recognition criteria; and
(iv) if mote data correlating to the target-occurrence representative feature is found, provide the correlating mote data.
71. The system of claim 70, wherein the input-selector includes a machine.
72. The system of claim 70, wherein the input-selector includes a user.
73. The system of claim 70, wherein the pattern recognition criteria is automatically selected by the instructions in response to the target-occurrence.
74. The system of claim 70, wherein the input selection further includes a selection of a representative feature of the target-occurrence.
75. The system of claim 74, wherein the pattern recognition criteria is automatically selected by the instructions in response to the input-selected representative feature.
76. The system of claim 75, wherein the automatic search instruction includes using the pattern recognition criteria selected in response to the input-selected representative feature.
77. The system of claim 70, wherein the instructions further include (v) protect the plurality of instances of mote data stored in the local digital data storage device from unauthorized access.
78. An occurrence-data retrieval system, comprising:
(a) a computing device operable to communicate with a plurality of data storage devices, each data storage device being a respective part of a mote of a network of distributed remote motes and operable to store plurality of instances of mote data, and each instance of mote data representing a feature sensed by the mote and correlating to a respective occurrence; and
(i) 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;
(ii) in response to the tendered access authorization, determine if at least one of the input-selector and recipient have an access right;
(iii) automatically select a pattern recognition criteria corresponding to the representative feature of the target-occurrence;
(iv) in response to the input selection corresponding to the target-occurrence, automatically search the plurality of instances of mote data stored in the data storage device for data correlating to the target-occurrence using the selected pattern recognition criteria; and
(v) if mote data correlating to the target-occurrence representative feature is found, and if at least one of the input-selector and recipient have an access right, provide the correlating mote data to the recipient.
79. The system of claim 78, wherein the input-selector includes a machine.
80. The system of claim 78, wherein the input-selector includes a user.
81. The system of claim 78, wherein the input-selector and the recipient are a same party.
82. The system of claim 78, wherein the recipient is a user.
83. The system of claim 78, wherein the recipient is a program running on another computing device.
84. The system of claim 78, wherein the data storage device includes a device selected from a group consisting of a local data storage device and a remote data storage device.
85. The system of claim 78, wherein the instructions further include, if neither the input-selector and recipient have an access right, not providing the correlating mote data.
86. The system of claim 78, further including an information security measure protecting the mote data stored in the data storage device from unauthorized access.
87. The system of claim 86, wherein the information security access allows access to the mote data upon receipt of a recognized data entry by the input-selector.
88. The system of claim 87, wherein the recognized data entry includes a password.
89. The system of claim 86, wherein the information security access allows access to the mote data upon receipt of a recognized biometric.
90. The system of claim 89, wherein the biometric includes at least one selected from a group consisting of a voice, finger, hand, face, and eye recognition.
91. An occurrence-data retrieval system, comprising:
(a) a data storage operable to store a plurality of instances of mote data, each instance of mote data correlating to at least one representative feature of a respective occurrence and including an associated tentative occurrence-identifier;
(b) a computing device operable to communicate with at the least one data storage device;
(c) an information security measure that controls access to the stored plurality of instances of mote data; and
(d) instructions, which when implemented in a computing device, cause the computing device to perform steps including:
(i) store the plurality of instances of mote data in the data storage;
(ii) receive from an input-selector an input selection corresponding to a target tentative occurrence-identifier, and a tender of access authorization;
(iii) in response to the input selection corresponding to the target tentative occurrence-identifier, automatically search the plurality of instances of mote data for tentative occurrence-identifiers correlating to the target tentative occurrence-identifier; and
(iv) if mote data correlating to the target tentative occurrence-identifier is found, and if at least one of the input-selector and recipient possess an access right, provide the correlating mote data.
92. The system of claim 91, wherein the data storage device includes a device selected from a group consisting of a local data storage device and a remote data storage device.
93. The system of claim 91, wherein the instructions further include, if neither the input-selector and recipient have an access right, not providing the correlating mote data.
94. The system of claim 91, further including an information security measure protecting the mote data stored in the data storage device from unauthorized access.
(a) receiving from an input-selector an input selection corresponding to a target-occurrence having at least one representative feature;
(b) automatically searching a plurality of instances mote data for mote data correlating to the target-occurrence, each instance of mote data correlating to an occurrence having at least one representative feature; and
(c) if mote data correlating to the target-occurrence is found, providing the correlating mote data;
wherein the providing further includes indicating a ranking of at least two instances of the correlating mote data in a hierarchy of the found correlating mote data.
96. The method of claim 95, wherein the plurality of instances of mote data are stored in a data storage.
97. The method of claim 96, wherein the data storage includes a device selected from a group consisting of a data storage device local to the computing device and a data storage device remote to the computing device.
98. A communication storage media containing computer instructions which, when run on a computing device, cause the computing device to perform the method comprising:
(a) receiving from an input-selector an input selection corresponding to a target- occurrence having at least one representative feature;
(b) automatically searching a plurality of instances mote data for mote data correlating to the target-occurrence. each instance of mote data correlating to an occurrence having at least one representative feature; and
(c) if mote data correlating to the target-occurrence is found, providing the correlating mote data.
99. A method implemented in a computing device, the method comprising:
(a) receiving an input selection from an input-selector corresponding to a target-occurrence having a representative feature, a recipient selection, and a tendered access authorization;
(b) automatically selecting a pattern recognition criteria corresponding to the representative feature of the target-occurrence;
(c) in response to the input selection corresponding to the target-occurrence, automatically searching a plurality of instances of correlated mote data for data correlating to the target-occurrence using the selected pattern recognition criteria, each instance of mote data correlating to at least one representative feature of a respective occurrence;
(d) in response to the tendered access authorization, determine if at least one of the input-selector and the recipient posses an access right; and
(e) if mote data correlating to the target-occurrence representative feature is found, and if at least one of the input-selector and the recipient have an access right, providing the correlating mote data.
100. The method of claim 99, wherein the plurality of instances correlated mote data are stored in a data storage device.
101. The method of claim 100, wherein the data storage device is local to the computing device.
102. A method implemented in a computing device, the method comprising:
(a) receiving an input selection from an input-selector corresponding to a tentative target-occurrence identifier, the target-occurrence having at least one representative feature, a recipient selection, and a tendered access authorization;
(b) in response to the input selection corresponding to the tentative target-occurrence identifier, automatically searching a plurality of instances of stored correlated mote data for data correlating to the tentative target-occurrence identifier; and
(c) if mote data correlating to the tentative target-occurrence identifier is found, providing the correlating mote data.
103. The method of claim 102, wherein the instances of correlated mote data are stored in a data storage device local to the computing device.
104. A data retrieval system, comprising:
(a) a local data storage device operable to store mote data sets from remote data storage devices, each mote data set representing a respective feature sensed by its respective remote storage device and having a correlation to a reference;
(b) a computing device operable to communicate with a local data storage device; and
(c) instructions, which when implemented in a computing device, cause the computing device to perform steps including:
(i) receive from an input-selector an input selection corresponding to a target-reference;
(ii) automatically search the instances of mote data stored in the data storage device for mote data correlating to the target-reference; and
(iii) if mote data correlating to the target-reference is found, provide the correlating mote data. Description
The present application is related to, claims the earliest available effective filing date(s) from the following listed application(s) (the “Related Applications”) (e.g., claims earliest available priority dates for other than provisional patent applications or claims benefits under 35 USC §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 clainis 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).
1. For purposes of the USPTO extra-statutory reiuirements. the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/816,375. entitled MOTE-ASSOCIATED INDEX CREATION, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 31 Mar. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
2. For purposes of the USPTO extra-statutory requirements. the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/816,082. entitled TRANSMISSION OF MOTE-ASSOCIATED INDEX DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 31 Mar. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
3. For purposes of the USPTO extra-statutory requirements. the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/816,358, entitled AGGREGATING MOTE-ASSOCIATED INDEX DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 31 Mar. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
4. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/816,102, entitled TRANSMISSION OF AGGREGATED MOTE-ASSOCIATED INDEX DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 31 Mar. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
5. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/816,364, entitled FEDERATING MOTE-ASSOCIATED INDEX DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 31 Mar. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
6. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/813,967, entitled MOTE NETWORKS HAVING DIRECTIONAL ANTENNAS, naming Clarence T. Tegreene as inventor, filed 31 Mar. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
7. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/814,454, entitled MOTE NETWORKS USING DIRECTIONAL ANTENNA TECHNIQUES, naming Clarence T. Tegreene as inventor, filed 31 Mar. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
8. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/844,613, entitled MOTE-ASSOCIATED LOG CREATION, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 12 May 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
9. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/844,564, entitled TRANSMISSION OF MOTE-ASSOCIATED LOG DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 12 May 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
10. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/844,612, entitled AGGREGATING MOTE-ASSOCIATED LOG DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 12 May 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
11. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/844,614, entitled TRANSMISSION OF AGGREGATED MOTE-ASSOCIATED LOG DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 12 May 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
12. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/843,987, entitled FEDERATING MOTE-ASSOCIATED LOG DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 12 May 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
13. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/850,914, entitled USING MOTE-ASSOCIATED LOGS, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 21 May 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
14. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/877,109, entitled USING FEDERATED MOTE-ASSOCIATED LOGS, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 25 Jun. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
15. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/877,099, entitled FREQUENCY REUSE TECHNIQUES IN MOTE-APPROPRIATE NETWORKS, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 25 Jun. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
16. For purposes of the USPTO extra-statutory reiuirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/882,119, entitled MOTE-APPROPRIATE NETWORK POWER REDUCTION TECHNIQUES, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 30 Jun. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
17. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/900,147, entitled USING FEDERATED MOTE-ASSOCIATED INDEXES, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 27 Jul. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
18. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/900,163, entitled USING MOTE-ASSOCIATED INDEXES, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 27 Jul. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filina date.
19. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/909,200, entitled DISCOVERY OF OCCURRENCE-DATA, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 30 Jul. 2004, which is currently co-pending, or is an application of which a currently co-rending application is entitled to the benefit of the filing date.
20. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. Pat. application No. 10/903,652, entitled DATA STORAGE FOR DISTRIBUTED SENSOR NETWORKS, naming Edward K. Y. Jung and Clarence T. Tegreene as inventors, filed 30 Jul. 2004, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date.
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.
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.
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.
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.
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.
An embodiment provides a data system. The system includes a computing device operable to communicate with a local data storage device and with a remote data storage operable to store a plurality of instances of sensor data. Each instance of sensor data respectively correlating to an occurrence, and instructions, which when implemented in a computing device, cause the computing device to perform steps. The steps include receive the plurality of instances of sensor data from the remote digital storage device, store the received plurality of instances of correlated sensor data in the local data storage device, and receive an input selection corresponding to a target-occurrence having at least one representative feature. Also, automatically search the stored plurality of instances of sensor data for sensor data correlating to the target-occurrence. If sensor data correlating to the target-occurrence is found, provide the correlating sensor data. The search for sensor data correlating to the target-occurrence may include correlating to at least one representative feature of the occurrence. An occurrence may include a real world event, a real world incident, a change in a data sequence, and a change in a time domain. The plurality of instances of sensor data may include a first instance of sensor data correlating to a respective first occurrence and a second instance of sensor data correlating to a respective second occurrence. The first and second occurrences may have happened at a substantially a same time, and/or at a substantially a same location. The first and second occurrences may include an at least substantially similar representative feature. The sensor data of the first occurrence may relate a first matter and the sensor data of the second occurrence may relate to a second matter. The first and second matters may be substantially similar. Each instance of sensor data may include a respective sequence, and the sequence may include a chronological sequence.
Another embodiment provides an occurrence-data archival system. The system includes a computing device operable to communicate with an archival data storage and a plurality of remote data storages, and instructions, which when implemented in a computing device, cause the computing device to perform steps. The steps include receive a plurality of instances of correlated sensor data from the plurality of remote data storages, each instance of sensor data respectively correlating to an occurrence, and store the received plurality of instances of correlated sensor data in the archival data storage. The system may further include a local data storage device operable as the archival data storage. The correlating to an occurrence may include correlating to at least one representative feature of the occurrence. The at least one representative feature may include a sequence. An occurrence may include a real world event, and a real world incident. The plurality of instances of sensor data may include a first instance of sensor data correlating to a respective first occurrence and a second instance of sensor data correlating to a respective second occurrence. The sensor data of the first occurrence may relate a first matter and the sensor data of the second occurrence may relate to a second matter, and the first and second matters may be at least substantially similar. Each instance of sensor data may include a respective sequence, and the sequence may include a chronological sequence. Each instance of sensor data may have been acquired by at least one sensor node of a plurality of distributed sensor nodes. Each sensor node may be part of a network of sensor nodes. The system may further include an information security measure operable to protect the stored plurality of instances of sensor data. The information security measure may provide at least one selected from a group consisting of information confidentiality, information integrity, and access control. The instructions may further include delete a portion of the stored data from the at least one remote data storage.
At least one instance of the received sensor data may include an associated tentative occurrence-identifier. The tentative occurrence-identifier may be associated by a method. The method includes receiving an input selection corresponding to a target-occurrence having at least one representative feature, and automatically selecting a pattern recognition criteria corresponding to at least one representative feature of the target-occurrence. Also,
searching sensor data for data correlating to the target-occurrence using the selected pattern recognition criteria, and, if sensor data correlating to the target-occurrence representative feature is found, associating the tentative occurrence-identifier with the correlating sensor data. A yet further embodiment provides a method implemented in a computing device. The method includes receiving a plurality of instances of correlated sensor data from a plurality of remote data storages, each instance of sensor data respectively correlating to an occurrence. Also, storing the received plurality of instances of correlated sensor data in a local data storage device, and protecting the received plurality of instances of correlated sensor data with an information security measure. The receiving step further may include receiving the plurality of instances of sensor data in response to a data push. The method may further include requesting the plurality of instances of sensor data from the remote data storage. The information security measure may protect by providing at least one selected from a group consisting of information confidentiality, information integrity, and access control.
An occurrence may include a real world event, a real world incident, a change in a data sequence, and a change in a time domain. The plurality of instances of sensor data may include a first instance of sensor data correlating to a respective first occurrence and a second instance of sensor data correlating to a respective second occurrence. The first and second occurrences may have happened at substantially a same time, and/or at substantially a same location. The first and second occurrences may include an at least substantially similar representative feature. The sensor data of the first occurrence may relate a first matter and the sensor data of the second occurrence may relate to a second matter. The first and second matters may be substantially similar. Each instance of sensor data may include a respective sequence, and the sequence may include a chronological sequence. An occurrence may include a sequence, a representative feature, and an associated tentative occurrence-identifier. The method may further include deleting at least one of the plurality of instances of sensor data from its respective remote data storage.
An embodiment includes an occurrence-data retrieval system. The system includes a computing device operable to communicate with a data storage which is operable to store a plurality of instances of sensor data acquired from at least one sensor node of a sensor network, each instance of sensor data respectively correlating to an occurrence, 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, and automatically search the plurality of instances of sensor data stored in the data storage for sensor data correlating to the target-occurrence. If sensor data correlating to the target-occurrence is found, provide the correlating sensor data. Each occurrence may include a representative feature. The data storage may be remote or local to the computing device. The computing device may communicate with a remote data storage through an intermediate computing device. The provide instruction further may include provide the correlating sensor data to the input-selector, and/or a third party. The instructions further include receive the plurality of instances of sensor data from at least one remote data storage, and store the received plurality of instances of correlated sensor data in a local data storage.
Another embodiment provides an occurrence-data retrieval system. The system includes a local digital data storage device operable to store a plurality of instances of sensor data, each instance of sensor data correlating to at least one representative feature of an occurrence. The system further includes a central computing device operable to communicate with the local digital data storage device, 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 at least one representative feature, and automatically select a pattern recognition criteria corresponding to at least one representative feature of the target-occurrence. In response to the input selection corresponding to the target-occurrence, automatically search the plurality of instances of stored sensor data for data correlating to the target-occurrence using the selected pattern recognition criteria. If sensor data correlating to the target-occurrence representative feature is found, provide the correlating sensor data. An input-selector may include a machine, and a user. The pattern recognition criteria may be automatically selected by the instructions in response to the target-occurrence. The input selection further may include a selection of a representative feature of the target-occurrence. The pattern recognition criteria may be automatically selected by the instructions in response to the input-selected representative feature. The automatic search instruction may include using the pattern recognition criteria selected in response to the inputted representative feature. The instructions may further include protect the plurality of instances of sensor data stored in the at least one local digital data storage device from unauthorized access.
A yet further embodiment provides an occurrence-data retrieval system. The system includes a computing device operable to communicate with a data storage device having a plurality of instances of sensor data stored thereon, each instance of sensor data correlating to an occurrence, and an information security measure protecting the sensor data stored in the at least one selected data storage device from unauthorized access. The system also includes 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 at least one 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, and automatically select a pattern recognition criteria corresponding to at least one representative feature of the target-occurrence. In response to the input selection corresponding to the target-occurrence, automatically search the plurality of instances of sensor data stored in the data storage device for data correlating to the target-occurrence using the selected pattern recognition criteria. If sensor data correlating to the target-occurrence representative feature is found, and if at least one of the source and recipient have an access right, provide the correlating sensor data to the recipient. An input-selector may include a machine and a user. The input-selector and the recipient may be a same party. A recipient may be a user, and a machine. The data storage device may include a device selected from a group consisting of a local data storage device and a remote data storage device.
An embodiment provides an occurrence-data retrieval system. The system includes a data storage device having a stored plurality of instances of sensor data, each instance of sensor data correlating to at least one representative feature of an occurrence and including an associated tentative occurrence-identifier. Also, a computing device operable to communicate with at the least one data storage device, and an information security measure that controls access to the stored plurality of instances of sensor data. 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 tentative occurrence-identifier, and a tender of access authorization. In response to the input selection corresponding to the target tentative occurrence-identifier, automatically search the plurality of instances of sensor data for data correlating to the target tentative occurrence-identifier. If sensor data correlating to the target tentative occurrence-identifier is found, and if at least one of the source and recipient possess an access right, provide the correlating sensor data. The data storage device may include a device selected from a group consisting of a local data storage device and a remote data storage device.
A further embodiment provides a method. The method includes receiving from an input-selector an input selection corresponding to a target-occurrence having at least one representative feature. The method also includes automatically searching a plurality of instances of stored sensor data for sensor data correlating to the target-occurrence, each instance of sensor data correlating to an occurrence having at least one representative feature. If sensor data correlating to the target-occurrence is found, providing the correlating sensor data. The plurality of instances of sensor data may be stored in a data storage device. The data storage device may be selected from a group consisting of a data storage device local to the computing device and a data storage device remote to the computing device. The providing further may include indicating a rank for at least two instances of the correlating sensor data in a hierarchy of the found correlating sensor data.
A yet further embodiment provides a method implemented in a computing device. The method includes receiving an input selection from an input-selector corresponding to a target-occurrence having at least one representative feature, a recipient selection, and a tendered access authorization. The method further includes automatically selecting a pattern recognition criteria corresponding to at least one representative feature of the target-occurrence. In response to the input selection corresponding to the target-occurrence, automatically searching a plurality of instances of stored correlated sensor data for data correlating to the target-occurrence using the selected pattern recognition criteria, each instance of sensor data correlating to at least one representative feature of an occurrence. In response to the tendered access authorization, determine if at least one of the input-selector and the recipient posses an access right. If sensor data correlating to the target-occurrence representative feature is found, and if at least one of the input-selector and the recipient have an access right, providing the correlating sensor data. The instances of stored correlated sensor data may be stored in a data storage device local to the computing device.
An embodiment provides a method implemented in a computing device. The method includes receiving an input selection from an input-selector corresponding to a tentative target-occurrence identifier, the target-occurrence having at least one representative feature, a recipient selection, and a tendered access authorization. In response to the input selection corresponding to the tentative target-occurrence identifier, automatically searching a plurality of instances of stored correlated sensor data for data correlating to the tentative target-occurrence identifier. If sensor data correlating to the tentative target-occurrence identifier is found, providing the correlating sensor data. The instances of stored correlated sensor data may be stored in a data storage device local to the computing device.
Another embodiment provides an occurrence-data archival system. The system includes a computing device operable to communicate with an archival data storage and a plurality of remote data storage devices, and instructions, which when implemented in a computing device, cause the computing device to perform steps. A step includes receive a plurality of sensor data sets from remote data storage devices, each sensor data set representing a respective feature sensed by its respective remote storage device, the respective feature having a correlation to a reference. Another step includes store the received plurality of sensor data sets in the archival data storage. The reference of a first data set may be a first occurrence. The reference of a second data set may be a second occurrence. The first occurrence and the second occurrence may be at least substantially the same.
A yet further embodiment provides a method implemented in a computing device. The method includes receiving a plurality of sensor data sets from remote data storage devices, each sensor data set representing a respective feature sensed by its respective remote storage device, the respective feature having a correlation to a reference. The method further includes storing the received plurality of sensor data sets in a local data storage device. The reference of a first data set may be a first occurrence, which may be a temporal reference related to the first occurrence. The reference of a second data set may be a second occurrence, which may be a temporal reference related to the second occurrence. The first occurrence and the second occurrence may be at least substantially the same. The method may further include protecting the received plurality of instances of correlated sensor data with an information security measure.
An embodiment provides a data retrieval system. The system includes a local data storage device operable to store sensor data sets from remote data storage devices, each sensor data set representing a respective feature sensed by its respective remote storage device, the respective feature having a correlation to a reference. The system also includes a computing device operable to communicate with a local data storage device, 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-reference, and automatically search the instances of sensor data stored in the data storage device for sensor data correlating to the target-reference. If sensor data correlating to the target-reference is found, provide the correlating sensor data.
A further embodiment provides a method. The method includes moving a portable central computing device to location such that the portable central computing device communicates with at least one sensor node of a network of distributed sensor nodes. Each sensor node is operable to sense and store a plurality of data sets each respectively representing a respective feature, each respective feature having a correlation to an occurrence. The method also includes causing the central computing device to acquire at least one sensor data set. The method may include selecting through the central computing device a sensor data set to be transmitted from the at least one sensor node to the central computing device. The method may include confirming that the at least one selected sensor data set is acquired by the central computing device. Each sensor node may include operability to filter the plurality of data sets for a feature correlating to a target-occurrence. Causing the central computing device to acquire at least one data set may include causing the central computing device to acquire at least one data set having a feature correlating to the target occurrence.
FIG. 1 illustrates a sensor node, or “mote;”
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;
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.”
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.
As used herein, the term “mote” typically means a semi-autonomous computing, communication, and/or sensing device as described in the mote literature (e.g., Intel Corporation's mote literature), as well as equivalents recognized by those having skill in the art (e.g., Intel Corporation's smart dust projects). Mote 100 depicts a specific example of a more general mote.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.”
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.
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.
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.
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.
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.
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.
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.
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.
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.
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and method for delivering data in an intermittent communication environment* Cited by examinerClassifications U.S. Classification1/1, 707/E17.005, 707/999.204, 707/999.003International ClassificationG06F17/30Cooperative ClassificationY10S707/99933, G06F17/30241, Y10S707/99955European ClassificationG06F17/30LLegal EventsDateCodeEventDescriptionAug 23, 2004ASAssignmentOwner name: SEARETE LLC, WASHINGTONFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:JUNG, EDWARD K.Y.;TEGREENE, CLARENCE T.;REEL/FRAME:015704/0749;SIGNING DATES FROM 20040813 TO 20040817Nov 6, 2009ASAssignmentOwner name: INVENTION SCIENCE FUND I, WASHINGTONFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SEARETE LLC;REEL/FRAME:023484/0640Effective date: 20091106Feb 2, 2010CCCertificate of correctionMar 30, 2012FPAYFee paymentYear of fee payment: 4Mar 2, 2015ASAssignmentOwner name: FORTRESS CREDIT CO LLC, NEW YORKFree format text: SECURITY INTEREST;ASSIGNOR:TRIPLAY, INC.;REEL/FRAME:035120/0862Effective 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