WIRELESS NETWORK CONNECTIVITY PREDICTION BASED ON USER PATTERNS AND BEHAVIOR

• US 20140287773A1
• Filed: 03/30/2012
• Published: 09/25/2014
• Est. Priority Date: 03/30/2012
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Agerstam, Mats, Srikanteswara, Srikathyayani Kathyayani
• Granted Patent: US 9,883,331 B2
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: G01S 5/0252 Radio frequency fingerprinting
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 48/16 Discovering, processing access restriction or access information
• CPC Class Codes: H04W 48/18 Selecting a network or a communication service
• CPC Class Codes: H04W 52/0251 using monitoring of local events, e.g. events related to user activity
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

Disclosed in some examples is a method including determining a plurality of user locations, the plurality of user locations determined based upon a clustering of wireless networks scanned by a network interface card communicatively coupled to the processor during an active period of the processor; determining a current location of the user, the current location being one of the plurality of user locations; and scanning for a plurality of wireless networks associated with one or more next locations, the one or more next locations selected from the plurality of user locations based upon the current user location and a measured probability.

Claims

1. A method comprising:

determining a plurality of user locations using one or more processors, the plurality of user locations determined based upon a clustering of wireless networks scanned by a network interface card communicatively coupled to the processor during an active period of the processor;

determining a current location of the user, the current location being one of the plurality of user locations; and

scanning for a plurality of wireless networks associated with one or more next locations, the one or more next locations selected from the plurality of user locations based upon the current user location and a measured probability.

2. The method of claim 1, comprising determining the current location of the user based upon a comparison of a plurality of wireless networks currently scanned and a plurality of wireless networks associated with the plurality of user locations.

3. The method of claim 1, wherein determining a plurality of user locations using one or more processors comprises:

creating a plurality of traces, each trace containing a list of a plurality of wireless networks scanned during a particular period of time;

dividing the plurality of traces into a plurality of segments, each segment comprising at least two traces, the at least two traces grouped into the segment based upon a uniformity of the wireless networks associated with the at least two segments; and

creating a plurality of clusters comprised of at least two segments based upon a similarity of the at least two segments, each of the plurality of clusters corresponding to a location.

4. The method of claim 3, wherein the plurality of clusters are created using a K-means algorithm.

5. The method of claim 3, comprising determining a plurality of wireless networks associated with the plurality of clusters based upon a determination that the wireless networks associated with the plurality of clusters are likely to be available at the associated location.

6. The method of claim 5, wherein the determination that the wireless networks associated with the plurality of clusters are likely to be available at the associated location comprises determining that the wireless networks associated with a particular location are present in that location above a threshold amount of times.

7. The method of claim 1, wherein the wireless networks are wireless networks operating in accordance with an 802.11 family of standards.

8. The method of claim 1, comprising calculating a transition matrix based upon the clustering of similar wireless networks scanned and wherein determining the one or more next locations comprises determining the most probable next locations based upon the transition matrix and the current user location.

9. The method of claim 1, wherein scanning for the plurality of wireless networks is done in response to powering down the processor into a low power state.

10. The method of claim 1, wherein responsive to finding one of the plurality of networks associated with one or more next locations, connecting to the found one or more of the plurality of networks.

11. The method of claim 10, comprising waking up one or more of the processors and notifying one or more applications to synchronize data over the connected wireless network responsive to connecting to the found one or more of the plurality of networks.

12. A computing device comprising:

one or more processors configured to;

determine a plurality of user locations, the plurality of user locations determined based upon a clustering of wireless networks scanned by a network interface card communicatively coupled to the processor during an active period of the processor;

determine a current location of the user, the current location being one of the plurality of user locations; and

scan for a plurality of wireless networks associated with one or more next locations, the one or more next locations selected from the plurality of user locations based upon the current user location and a measured probability.

13. The computing device of claim 12, wherein the one or more processors are configured to determine the current location of the user based upon a comparison of a plurality of wireless networks currently scanned and a plurality of wireless networks associated with the plurality of user locations.

14. The computing device of claim 12, wherein the one or more processors are configured to determine a plurality of user locations by:

creating a plurality of traces, each trace containing a list of a plurality of wireless networks scanned during a particular period of time;

dividing the plurality of traces into a plurality of segments, each segment comprising at least two traces, the at least two traces grouped into the segment based upon a uniformity of the wireless networks associated with the at least two segments; and

creating a plurality of clusters comprised of at least two segments based upon a similarity of the at least two segments, each of the plurality of clusters corresponding to a location.

15. The computing device of claim 14, wherein the plurality of clusters are created using a K-means algorithm.

16. The computing device of claim 14, wherein the one or more processors are configured to determine a plurality of wireless networks associated with the plurality of clusters based upon a determination that the wireless networks associated with the plurality of clusters are likely to be available at the associated location.

17. The computing device of claim 16, wherein the one or more processors are configured to determine that the wireless networks associated with the plurality of clusters are likely to be available at the associated location by determining that the wireless networks associated with a particular location are present in that location above a threshold amount of times.

18. The computing device of claim 12, wherein the wireless networks are wireless networks operating in accordance with an 802.11 family of standards.

19. The computing device of claim 12, wherein the one or more processors are configured to calculate a transition matrix based upon the clustering of similar wireless networks scanned and wherein the one or more processors are configured to determine the one or more next locations by determining the most probable next locations based upon the transition matrix and the current user location.

20. The computing device of claim 12, wherein the one or more processors are configured to scan for the plurality of wireless networks in response to powering down the one or more processors into a low power state.

21. The computing device of claim 12, wherein the one or more processors are configured to connect to the found one or more of the plurality of networks in response to finding one of the plurality of networks associated with one or more next locations.

22. The computing device of claim 21, wherein the one or more processors are configured to wake up and notify one or more applications to synchronize data over the connected wireless network responsive to connecting to the found one or more of the plurality of networks.

23. A machine readable medium that stores instructions which when performed by a machine, cause the machine to perform operations comprising:

determine a plurality of user locations, the plurality of user locations determined based upon a clustering of wireless networks scanned by a network interface card communicatively coupled to the processor during an active period of the processor;

determine a current location of the user, the current location being one of the plurality of user locations; and

scan for a plurality of wireless networks associated with one or more next locations, the one or more next locations selected from the plurality of user locations based upon the current user location and a measured probability.

24. The machine readable medium of claim 23, wherein the instructions for determining the current location of the user include instructions, which when performed by the machine, cause the machine to determine the current location of the user based upon a comparison of a plurality of wireless networks currently scanned and a plurality of wireless networks associated with the plurality of user locations.

25. The machine readable medium of claim 23, wherein the instructions for determining a plurality of user locations comprise instructions which when performed by the machine, cause the machine to perform the operations of:

creating a plurality of traces, each trace containing a list of a plurality of wireless networks scanned during a particular period of time;

dividing the plurality of traces into a plurality of segments, each segment comprising at least two traces, the at least two traces grouped into the segment based upon a uniformity of the wireless networks associated with the at least two segments; and

creating a plurality of clusters comprised of at least two segments based upon a similarity of the at least two segments, each of the plurality of clusters corresponding to a location.

26. The machine readable medium of claim 25, wherein the instructions for creating a plurality of clusters include instructions which when performed by the machine cause the machine to create the plurality of clusters using a K-means algorithm.

27. The machine readable medium of claim 25, wherein the instructions for determining the plurality of wireless networks associated with the plurality of clusters comprises instructions, which when performed by the machine, cause the machine to determine the plurality of wireless networks based upon a determination that the wireless networks associated with the plurality of clusters are likely to be available at the associated location.

28. The machine readable medium of claim 27, wherein the instructions for determining that the wireless networks associated with the plurality of clusters are likely to be available at the associated location includes instructions which when performed by the machine causes the machine to perform the operations of determining that the wireless networks associated with a particular location are present in that location above a threshold amount of times.

29. The machine readable medium of claim 23, wherein the wireless networks are wireless networks operating in accordance with an 802.11 family of standards.

30. The machine readable medium of claim 23, wherein the instructions include instructions which when performed by the machine, cause the machine to perform the operations of calculating a transition matrix based upon the clustering of similar wireless networks scanned and wherein the instructions for determining the one or more next locations include instructions, which when performed by the machine cause the machine to perform the operations of determining the most probable next locations based upon the transition matrix and the current user location.

31. The machine-readable medium of claim 23, wherein the instructions include instructions which when performed by the machine cause the machine to scan for the plurality of wireless networks in response to powering down a processor into a low power state.

32. The machine-readable medium of claim 23, wherein the instructions include instructions which when performed by the machine cause the machine to perform the operations of:

responsive to finding one of the plurality of networks associated with one or more next locations, connecting to the found one or more of the plurality of networks.

33. The machine-readable medium of claim 32, wherein the instructions include instructions which when performed by the machine cause the machine to perform the operations of:

waking up the processor and notifying one or more applications to synchronize data over the connected wireless network responsive to connecting to the found one or more of the plurality of networks.

34. An apparatus comprising:

a user location module configured to;

determine a plurality of user locations, the plurality of user locations determined based upon a clustering of wireless networks seen by a computing device during an active period of the computing device; and

determine a current location of the user, the current location being one of the plurality of user locations;

a selection module, configured to select one or more next locations from the plurality of user locations based upon the current user location and a measured probability; and

an output module, configured to output the plurality of wireless networks associated with one or more next locations to a network interface card.

35. The apparatus of claim 34, wherein the user location module is configured to determine the current location of the user based upon a comparison of a plurality of wireless networks currently scanned and a plurality of wireless networks associated with the plurality of user locations.

36. The apparatus of claim 34, wherein the user location module is configured to determine a plurality of user locations by:

creating a plurality of traces, each trace containing a list of a plurality of wireless networks scanned during a particular period of time;

dividing the plurality of traces into a plurality of segments, each segment comprising at least two traces, the at least two traces grouped into the segment based upon a uniformity of the wireless networks associated with the at least two segments; and

creating a plurality of clusters comprised of at least two segments based upon a similarity of the at least two segments, each of the plurality of clusters corresponding to a location.

37. The apparatus of claim 36, wherein the user location module is configured to determine a plurality of wireless networks associated with the plurality of clusters based upon a determination that the wireless networks associated with the plurality of clusters are likely to be available at the associated location.

38. The apparatus of claim 37, wherein the user location module is configured to determine that the wireless networks associated with the plurality of clusters are likely to be available at the associated location by determining that the wireless networks associated with a particular location are present in that location above a threshold amount of times.

39. The apparatus of claim 34, wherein the selection module is configured to calculate a transition matrix based upon the clustering of similar wireless networks scanned and wherein the selection module is configured to determine the one or more next locations by determining the most probable next locations based upon the transition matrix and the current user location.