Aggregation and retrieval of network sensor data

• US 20090119267A1
• Filed: 09/30/2008
• Published: 05/07/2009
• Est. Priority Date: 03/31/2004
• Status: Active Grant

• Current Assignee: Alarm.com Inc. (Alarm.com Holdings, Inc.)
• Original Assignee: The Invention Science Fund I, L.L.C. (Intellectual Ventures LLC)
• Inventors: Tegreene, Clarence T., Jung, Edward K.Y.
• Granted Patent: US 8,271,449 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99955 Archiving or backup

Abstract

Methods, 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 another approach, an exemplary method includes receiving from an input-selector an input selection corresponding to a target-occurrence having at least one representative feature, and searching stored sensor data for sensor data correlating to the target-occurrence.

Claims

1-21. -21. (canceled)

22. An occurrence-data archival system, comprising:

a computing device operable to communicate with a local data storage and one or more remote data storages; and

instructions that cause the computing device to perform a process including;

receiving a plurality of instances of correlated mote data from the one or more remote data storages, each instance of mote data respectively correlating to an occurrence; and

storing the received plurality of instances of correlated mote data in the archival data storage.

23. The system of claim 22, further including 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 process includes deleting a portion of the stored data from the at least one remote data storage.

40. The system of claim 22, wherein at least one instance of the received mote data includes an associated tentative occurrence-identifier.

41. The system of claim 40, 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;

searching 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.

42-111. -111. (canceled)

112. An occurrence-data archival system, comprising:

a computing device operable to communicate with an archival data storage and remote data storage devices; and

instructions that cause the computing device to perform a process, including;

receiving one or more mote data sets from at least one of the remote data storage devices, each mote data set representing a respective feature sensed by its respective remote storage device, the respective feature having a correlation to a reference; and

storing the one or more mote data sets in the archival data storage.

113. The system of claim 112, wherein the reference of a first data set is a first occurrence.

114. The system of claim 112, wherein the reference of a second data set is a second occurrence.

115. The system of claim 114, wherein the first occurrence and the second occurrence are at least substantially the same.

116. A method implemented in a computing device, the method comprising:

receiving a plurality of mote data sets from at least one remote data storage device, each sensor data set representing a respective feature sensed by its respective remote storage device, the respective feature having a correlation to a reference;

storing the received plurality of mote data sets in a local data storage device; and

applying an information security measure to the stored plurality of mote data sets.

117. The method of claim 116, wherein the reference of a first data set is a first occurrence.

118. The method of claim 117, wherein the first reference is a temporal reference related to the first occurrence.

119. The method of claim 116, wherein the reference of a second data set is a second occurrence.

120. The method of claim 119, wherein the second reference is a temporal reference related to the second occurrence.

121. The method of claim 119, wherein the first occurrence and the second occurrence are at least substantially the same.

122. The method of claim 116, further including protecting the received plurality of instances of correlated mote data with an information security measure.

123. (canceled)

124. A machine-implemented method, comprising:

receiving a target-occurrence having at least one representative feature;

automatically searching one or more instances of mote data for mote data correlating to the target-occurrence, each instance of mote data corresponding to an occurrence; and

providing the mote data correlating to the target-occurrence.

125. The method of claim 124, wherein automatically searching one or more instances of mote data for mote data correlating to the target-occurrence includes correlating to at least one representative feature of the occurrence.

126. The method of claim 124, wherein automatically searching one or more instances of mote data includes automatically searching one or more instances of mote data, wherein at least one instance of mote data corresponds to an occurrence that includes a real world event.

127. The method of claim 124, wherein automatically searching one or more instances of mote data includes automatically searching one or more instances of mote data, wherein at least one instance of mote data corresponds to an occurrence that includes a real world incident.

128. The method of claim 124, wherein automatically searching one or more instances of mote data includes automatically searching one or more instances of mote data, wherein at least one instance of mote data corresponds to an occurrence that includes a change in a data sequence.

129. The method of claim 124, wherein automatically searching one or more instances of mote data includes automatically searching one or more instances of mote data, wherein at least one instance of mote data corresponds to an occurrence that includes a change in a time domain.

130. The method of claim 124, wherein automatically searching one or more instances of mote data includes automatically searching 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.

131. The method of claim 124, wherein automatically searching one or more instances of mote data includes automatically searching a first instance of mote data correlating to a first matter and a second instance of mote data correlating to a second matter.

132. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having at least one frequency characteristic.

133. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having at least one acoustic frequency characteristic.

134. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having at least one acoustic signature of a known event.

135. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having at least one light frequency characteristic.

136. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having at least one amplitude characteristic.

137. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having a frequency characteristic and an amplitude characteristic.

138. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having at least one magnetic characteristic.

139. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having at least one repetitive sequence.

140. The method of claim 124, wherein receiving a target-occurrence having at least one representative feature includes receiving a target-occurrence having at least one chronological sequence.