MULTISENSORY CHANGE DETECTION FOR INTERNET OF THINGS DOMAIN

• US 20160095013A1
• Filed: 12/22/2014
• Published: 03/31/2016
• Est. Priority Date: 09/26/2014
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Faivishevsky, Lev, Armon, Amitai
• Granted Patent: US 10,356,649 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G08C 25/00 Arrangements for preventing or correcting errors; Monitoring arrangements
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/125 involving control of end-device applications over a network
• CPC Class Codes: H04Q 9/00 Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
• CPC Class Codes: H04W 28/021 in wireless networks with changing topologies, e.g. ad-hoc networks self-organizing networks H04W84/18
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

Examples of systems and methods for multisensory change detection are generally described herein. A method may include receiving a first set of signals from a first combination of sensors and a second set of signals from a second combination of sensors in a plurality of sensors, and determining a first distribution for the first set of signals and a second distribution for the second set of signals. The method may include estimating a divergence between the first and second distributions using the first and second combinations of sensors, a count of the plurality of sensors, and distances from a plurality of signals in the second set of signals to a first plurality of nearest neighbor signals in the first set of signals and a second plurality of nearest neighbor signals in the second set of signals. The method may include determining whether the divergence exceeds a threshold.

Claims

1. A multisensory change detection system comprising:

a communication module to receive a first set of signals from a first combination of sensors in a plurality of sensors and receive a second set of signals from a second combination of sensors in the plurality of sensors; and

a processing module to;

determine a first distribution for the first set of signals and a second distribution for the second set of signals;

estimate a divergence between the first distribution and the second distribution based at least in part on a count of the first combination of sensors, a count of the second combination of sensors, a count of the plurality of sensors, distances from a plurality of signals in the second set of signals to a first plurality of nearest neighbor signals in the first set of signals and distances from a plurality of signals in the second set of signals to a second plurality of nearest neighbor signals in the second set of signals; and

determine whether the divergence exceeds a threshold.

2. The system of claim 1, wherein the processing module is further to generate an alert in response to determining the divergence exceeds the threshold.

3. The system of claim 1, wherein the first plurality of nearest neighbor signals and the second plurality of nearest neighbor signals include fewer signals than the first set of signals and the second set of signals respectively.

4. The system of claim 1, wherein the divergence is estimated using distances from a plurality of signals in the first set of signals to a third plurality of nearest neighbor signals in the first set of signals and a fourth plurality of nearest neighbor signals in the second set of signals.

5. The system of claim 1, wherein the first combination of sensors and the second combination of sensors are the same.

6. The system of claim 1, wherein the second set of signals are received immediately preceding the first set of signals.

7. The system of claim 1, wherein to estimate the divergence, the processing module is further to estimate a number of additional bits required to code the first set of signals using a code based on the second distribution.

8. The system of claim 1, wherein the processing module is further to determine the threshold using a target false alarm rate.

9. The system of claim 8, wherein the processing module is to determine the target false alarm rate recursively using a ratio of false alarm rates to correct alarm rates.

10. The system of claim 1, wherein the second combination of sensors is selected by a user.

11. The system of claim 1, wherein the communication module is further to receive characteristic data from a third combination of sensors at a first period and a second period, the characteristic data representing sensor states;

wherein the processing module is further to analyze the first and second periods to determine a difference between the sensor states of the third combination of sensors at the first period and the second period; and

wherein the processing module is to use the determined difference to estimate the divergence.

12. The system of claim 11, wherein the sensor states includes at least one of a sensor location, a sensor angle, a sensor orientation, a sensor movement, or a sensor operational status.

13. The system of claim 1, wherein the communication module is further to receive characteristic data from the plurality of sensors at a first period and a second period, the characteristic data representing environmental states;

wherein the processing module is further to analyze the first and second periods to determine a difference between the environmental states of the plurality of sensors at the first period and the second period; and

wherein the processing module is to use the determined difference to estimate the divergence.

14. The system of claim 13, wherein the environmental states includes information about at least one of wind speed, temperature, heat index, humidity, movement, luminosity, signal strength, audio, video, chemical composition, or external device functionality.

15. The system of claim 1, wherein the first combination of sensors is selected by a user.

16. A method for multisensory change detection comprising:

receiving, at a device, a first set of signals from a first combination of sensors in a plurality of sensors;

receiving, at the device, a second set of signals from a second combination of sensors in the plurality of sensors;

determining, at the device, a first distribution for the first set of signals;

determining, at the device, a second distribution for the second set of signals;

estimating, at the device, a divergence between the first distribution and the second distribution based at least in part on the first combination of sensors, the second combination of sensors, a total count of the plurality of sensors, distances from a plurality of signals in the second set of signals to a first plurality of nearest neighbor signals in the first set of signals, and distances from a plurality of signals in the second set of signals to a second plurality of nearest neighbor signals in the second set of signals; and

determining, at the device, whether the divergence exceeds a threshold.

17. The method of claim 16, further comprising generating an alert in response to determining the divergence exceeds the threshold.

18. The method of claim 16, further comprising, determining the threshold using a target false alarm rate.

19. The method of claim 18, further comprising, determining the target false alarm rate recursively using a ratio of false alarm rates to correct alarm rates.

20. The method of claim 16, further comprising:

receiving characteristic data from a third combination of sensors at a first period and a second period, the characteristic data representing sensor states or environmental states; and

analyzing the first and second periods to determine a difference between the sensor states or the environmental states of the third combination of sensors at the first period and the second period; and

wherein estimating the divergence includes using the determined difference.

21. The method of claim 20, wherein the sensor states includes at least one of a sensor location, a sensor angle, a sensor orientation, a sensor movement, or a sensor operational status.

22. The method of claim 20, wherein the environmental states includes information about at least one of wind speed, temperature, heat index, humidity, movement, luminosity, signal strength, audio, video, chemical composition, or external device functionality.

23. At least one machine-readable medium including instructions for receiving information, which when executed by a machine, cause the machine to:

receive, at the machine, a first set of signals from a first combination of sensors in a plurality of sensors;

receive, at the machine, a second set of signals from a second combination of sensors in the plurality of sensors;

determine, at the machine, a first distribution for the first set of signals;

determine, at the machine, a second distribution for the second set of signals;

estimate, at the machine, a divergence between the first distribution and the second distribution based at least in part on the first combination of sensors, the second combination of sensors, a total count of the plurality of sensors, distances from a plurality of signals in the second set of signals to a first plurality of nearest neighbor signals in the first set of signals and distances from a plurality of signals in the second set of signals to a second plurality of nearest neighbor signals in the second set of signals; and

determine, at the machine, whether the divergence exceeds a threshold.

24. The at least one machine-readable medium of claim 23, further comprising operations to generate an alert in response to determining the divergence exceeds the threshold.

25. The at least one machine-readable medium of claim 23, further comprising, operations to determine the threshold using a target false alarm rate.