Infrastructure for Context Driven Pervasive Computing Applications

1. Almost two thirds (63%) of Americans say it is annoying to hear ringing cell phones or cell phone chatter in public places. Ringing of cell phones is a big complaint and many times a cause of embarrassment in classrooms and meeting rooms How about making the environment smart so that it takes care of silencing the cell phones even if the users forget! Will save some embarrassment for sure, isn’t it?

2. Infrastructure for Context Driven Pervasive Computing Applications Presented by: Vishakha Gupta Advisor: Prof. Peter Steenkiste Reader: Prof. Raj Rajkumar

3. Agenda • Goals • Scenarios • Requirements • Concept • Thesis Statement • Motivation • Related Work • Architecture • Evaluation • History Information • Conclusion • Limitations • Future Work Information Networking Institute

4. Goals • In usual applications and services user has to initiate some action in order to use the results • Smart environment for a better user experience • Focus on area based user tracking in in-building environments • Less attention has been paid to the fundamental and challenging problem of providing capability to an application in defining physical areas • Determine with high probability when the user is in the area of interest to the application Information Networking Institute

5. Scenarios • When a person enters a secure building, devices monitor his motion and warn him if he goes in a prohibited area, through a warning message passed to his cell phone • In an auditorium, where a show or some program is going to begin, everyone’s cell phone is expected to be turned off or to be in silent mode • When people are seated in the airplane, as the plane is about to take off, the cell phones and other electronic devices (if possible) could receive an interrupt indicating they should turn themselves off. • At places such as a meeting hall, a classroom, a hospital, cell phone rings may cause disturbance. At the same time, people may want to attend their calls. So the devices should be signaled to change over to the silent mode Information Networking Institute

6. Requirements • A method to define the regions of interest to the application • An infrastructure to enable area based tracking for the client devices in an establishment • Need for downloading of software on a handheld or including the APIs necessary for communication in the handheld by the device manufacturers • Authenticity of the code getting downloaded and the source of the messages • No effect on the normal communication or device use • Interoperability and other challenges associated with a distributed system • Generic APIs to help accommodate new applications that are developed Information Networking Institute

7. Concept Information Networking Institute

8. Thesis Statement • Present an infrastructure for context-driven applications enabling them to specify area-based user tracking requirements • Ability to determine with high probability when the user is in the area of interest to the application Information Networking Institute

9. Motivation • Minimal change in the infrastructure. • Easy testing and deployment • No requirement to formulate a coordinate system • Flexibility to an application in defining the attributes of an area as needed • No restriction in terms of the shape of an area to be defined by the application Information Networking Institute

10. Related Work • Area aware computing - relatively new concept • Research work that comes really close to the concept presented in this thesis • Area based triggers by Hermann et. Al. • Fingerprinting using access points as in PlaceLab project by Intel Information Networking Institute

11. Related Work [2] • Other related projects • RADAR project for in-building user tracking by Microsoft Research • AURA hybrid space model by CMU • CRICKET project by MIT Information Networking Institute

12. Terminology • Zone – A physical area defined by an application for its use. For example, the area where cell phones must be turned to silent mode like in an auditorium • Region – Any area in a building which could be a zone • Signature – Tuple consisting of <Access Point MAC, RSSI_min, RSSI_max, Weight> used by the application to define zones in WiFi signal space • Client Signature - Tuple consisting of <Access Point MAC, SSID, RSSI heard> read by a client device from its wireless interface • Region Definition or Rule – A tuple of the form <Region ID, Signature> used in defining zones Information Networking Institute

13. Terminology [2] • Location – A tuple of the form <X, Y, Height, Description> defined keeping in mind a requirement of having actual Euclidean coordinates if needed anytime by the system. Currently we use the description member to make the location more meaningful wherever it is used. • Percentage Match – It’s the probability with which the client signature matches a zone definition using the algorithms described later • Message – It’s a string used as an attribute for the zone, defined by the application if it wants to convey something to the user when he is mapped to a certain zone • Action – The action that the application expects users to perform when they are in a certain zone Information Networking Institute

14. Architecture Information Networking Institute

15. Architecture – Offline Zone Definition • Repository • Stores access point, zone and rule information • Repository Manager • Interface for application to provide requisite information Information Networking Institute

16. Architecture – Offline Zone Definition [2] Information Networking Institute

17. Architecture – Server • Listener – Listens for client requests consisting of client signature • Rule Manager – • Uses rule and zone information from the Repository • Uses algorithm to find a matching zone for the current client signature • Sender – Sends the zone information to client with message and action Information Networking Institute

18. Architecture – Client • Wireless Device Reader – reads wireless card information and forms client signature • Sender – sends client signature to the server for zone matching • Listener – receives zone information from server • UI Component – interacts with the user if required by the application Information Networking Institute

19. Algorithms begin procedure find-match Let A be the best heard access point by the client Let L denote the client signature Let M denote the list of zones with signatures consisting of A loop for all the zones Z in M store best zone(s) found end loop return best heard zone(s) end procedure find-percent-match(Z, L) Information Networking Institute

20. Algorithms – Exact Match begin procedure find-percent-match count = number of access points in signature S used in definition of Z loop for all access points B used in S if (B exists in L and RSSI heard for it is within RSSI_MINA and RSSI_MAXA) increment match end loop percent = match /count * 100 return percent end procedure Information Networking Institute

21. Algorithms – Deviated Match begin procedure find-percent-match count = number of access points in signature S used in definition of Z drop_per_deviation = 0.2 loop for all access points B used in S if (B exists in L and RSSI heard for it is within RSSI_MINA and RSSI_MAXA) increment match else dMatch = (1 – (deviation in RSSI from MIN or MAX) * drop_per_deviation) if dMatch > 0 then match += dMatch end loop percent = match /count * 100 return percent end procedure Information Networking Institute

22. Algorithms – Weighted Match begin procedure find-percent-match count = number of access points in signature S used in definition of Z loop for all access points B used in S if (B exists in L and RSSI heard for it is within RSSI_MINA and RSSI_MAXA) match += WeightA end loop percent = match /count * 100 return percent end procedure Information Networking Institute

23. Algorithms – Weighted Deviation Match begin procedure find-percent-match loop for all access points B used in S if (B exists in L and RSSI heard for it is within RSSI_MINA and RSSI_MAXA) match += WeightA else dMatch = (1 – (deviation in RSSI) * drop_per_deviation) if dMatch > 0 then match += (dMatch * WeightA) else if (dMatch < 0) then dMatch = exp (-WeightA * deviation in RSSI / 100); match *= dMatch end loop percent = match /count * 100 return percent end procedure Information Networking Institute

24. Evaluation – Define Zones Information Networking Institute

25. Evaluation – RSSI Measurement Information Networking Institute

26. Evaluation – Entering Information • Room 105 (Area: 44.6 sq.m) – can be defined as • <00:02:2D:04:68:3B,-80,-70,1.5>; <00:60:1D:23:C5:B5,-80,-70,1.0>; <00:02:2D:51:A9:0A,-80,-70,1.5>; • Reception (Area: 38.64 sq.m) – can be defined as • <00:02:2D:04:68:3B,-80,-70,1.0>; <00:60:1D:23:C5:B5,-80,-70,1.25>; <00:02:2D:51:A9:0A,-65,-55,2.0>; • Room 127 (Area: 8.18 sq.m) – can be defined as • <00:02:2D:04:68:3B,-60,-50,2.0>; <00:60:1D:23:C5:B5,-75,-65,1.0>; <00:02:2D:51:A9:0A,-80,-70,1.0>; Information Networking Institute

27. Evaluation – Comparison of Algorithms Information Networking Institute

28. Evaluation – Comparison of Algorithms [2] Information Networking Institute

29. Evaluation – Comparison of Algorithms [3] Information Networking Institute

30. Evaluation - Conclusion • Deviated Match algorithm better than remaining with number of access points(N) > 3 • The number of access points determined by the zone under consideration • The Weighted Deviation Match algorithm shows more consistency in accuracy • Reduces spurious results acquired due to the exponential degradation • Example on next slide Information Networking Institute

31. Evaluation – Conclusion [2] Client signature at 1 – <00:02:2D:51:A9:0A, -76>; <00:02:2D:04:68:3B, -43>; <00:14:1B:5A:22:90, -89> Zone definition for Room 127 – <00:02:2D:04:68:3B,-60,-50,2.0>; <00:60:1D:23:C5:B5,-75,-65,1.0>; <00:02:2D:51:A9:0A,-80,-70,1.0>; Zone definition for Room 105 – <00:02:2D:04:68:3B,-80,-70,1.5>; <00:60:1D:23:C5:B5,-80,-70,1.0>; <00:02:2D:51:A9:0A,-80,-70,1.5>; • At one point, Room 105 matched at Position 1 by Exact Match, Deviation Match and Weighted Match • The Weighted Deviation Match algorithm showed Room 127 Information Networking Institute

32. Evaluation - Limitations • Only defined zone is Reception • But Rooms 101, 103 and Lobby also show a match for Reception using any of the four algorithms presented Information Networking Institute

33. Evaluation – Limitations [2] • Only defined zone is Room 127 • But the shaded portion on the floor plan also shows a match for Room 127 using any of the four algorithms presented Information Networking Institute

34. History Information • Use past area information to conclude strongly about the present position • Use bluetooth devices to conclude strongly about the present position • New term “Space” - An area that is not defined by the application but which could be of consequence in defining zones • E.g. A corridor in the building which might be leading to a zone • Clients modified to report any nearby bluetooth devices as well as the previous area matched with a timestamp Information Networking Institute

35. History – Using Space Information • Consider two applications • One has defined Zone Y • The other has defined Zone Z • At time t, the system knows that a device is in Space X (maybe by using one of the signature matching algorithms mentioned earlier) • At time t + 1, the client signature says Space X with timestamp t • Higher probability that user must be in Zone Y for application 1 while in Zone Z for application 2 • Example follows Information Networking Institute

36. History – Using Space Information [2] Information Networking Institute

37. History - Bluetooth Information Networking Institute

38. History – New Algorithm begin procedure find-percent-match Let bDevice represent a bluetooth device if timestamp of client signature and server do not differ by MARGIN if bDevice heard by client and bDevice identifies Z return complete match if Space S heard by client before this iteration and S leads to Z perform signature based match percent = (weight1 * space match percent + weight2 * signature match percent) / 2 return percent perform match as in previous signature based cases return percent match end procedure Information Networking Institute

39. Conclusions • Solution for area aware computing using existing infrastructure • Implemented and analyzed four algorithms for WiFi signal space matching of zones • Weighted Deviation Match algorithm works best in general • Improvement in identifying zones using history information Information Networking Institute

40. Limitations • Zone definitions in WiFi signal space • Configuration of access points • Performance at different times of day • Varying signal strength • Bluetooth • Chances of having Bluetooth devices installed Information Networking Institute

41. Future Work • Experimental evaluation of history information • Study the scalability of the system by introducing multiple clients • Account for network usage and computation requirements on the client • Implement an end to end system involving • Download and verification of software on multiple devices • Study the variation pattern in RSSI to define a model • Define a variable signature depending on time of day etc. constituting an intelligent zone definition Information Networking Institute

42. Information Networking Institute

43. Thank You Information Networking Institute

44. Implementation - Server Information Networking Institute

45. Implementation - Client Information Networking Institute