1. Introduction to Locating Systems in Ubiquitous Computing and Sensor Networks Amir Haghighat

2. Why location? Ubiquitous Computing (ubicomp) Context-aware computing Search and rescue Sensor Networks Environmental monitoring Geographic routing Target tracking

3. Man discovers mobile computing… Now, where’s the nearest place I can buy shoulder pads?! …and quickly wants location-enhanced computing.

4. Why not GPS? Ubicomp GPS does not work indoors GPS works poorly in urban canyons Sensor Networks Power, cost, and size issues

5. Outline Localization techniques Taxonomy Mini-survey of location systems in ubiquitous computing “Beep: 3D Indoor Positioning Using Audible Sound“ Locating systems in sensor networks

6. Location Sensing Techniques Triangulation Lateration Angulation Scene analysis Proximity

7. Lateration Time of flight Attenuation

8. Angulation Phased antenna arrays provide angle of arrival

9. Scene Analysis Uses features of a scene observed from a particular vantage point to draw conclusions about the location of the observer or of objects in the scene. No distance/angle measurements Two types of scene analysis: Static: observed features looked-up in predefined dataset that maps them to location(i.e. MSR RADAR) Differential: Differences in the scene correspond to movements of observer

10. Proximity Detecting physical contact (i.e. human skin) Monitoring wireless cellular access points Observing automatic ID systems (i.e. RFID tracking of livestock)

11. Location System Properties Physical Position vs. Symbolic Location Absolute vs. Relative Localized location computation (privacy and power issues) Accuracy and Precision i.e. 1 meter accuracy, 90% of time Scale Recognition Cost Time and money Limitations

12. Mini-survey of Location Systems in Ubiquitous Computing Media: infrared, (ultra)sound, radio frequency (RF), vision

13. Active Badge Users carry badges that emit diffuse infrared signals One base-station per room interference from fluorescent light and sunlight Olivetti Active Badge (right) and a base station (left)

14. Active Bat RF and ultrasound Lateration performed by central server 9cm 95% of time, 1 base-station per 10m 2

15. Cricket RF and ultrasound Privacy and decentralization in mind Symbolic or physical location 4*4 ft regions, ~100% of time, 1 beacon per 16 ft 2

16. RADAR 802.11 signal strengths from 3 APs construct a “signature” for every location “Offline phase” and “Online phase” 3 meter accuracy, 50% of time, having 3 APs

17. E911 FCC initiative 100m, 67% and 300m, 95% Possible solutions: GPS, proximity, angle of arrival, time difference of arrival Impacts: Network impact, handset impact, legacy handsets

18. Place Lab Uses 802.11 and GSM beacons, whose positions are known 802.11 AP locations from war drivers Over 2 million known AP positions GSM tower locations from FCC’s database 20-30m median accuracy, 100% coverage in Seattle GPS works less accurately in urban areas (i.e. downtown)

19. Bayes Filter

20. Easy Living Real-time 3D cameras provide stereo- vision positioning for home environment Move from person tracking to capturing broader context

21. CSEM (www.csem.ch) The camera emits an RF modulated optical radiation field (typically 20 MHz or higher) in the infra-red spectrum. This signal is diffusely backscattered by the scene and detected by the camera. Every pixel is able to demodulate the signal and detect its phase, which is proportional to the distance of the reflecting object.

22. Beep: 3D Indoor Positioning Using Audible Sound Atri Mandal, Cristina V. Lopes, Tony Givargis, Amir Haghighat, Raja Jurdak, Pierre Baldi School of Information and Computer Sciences University of California, Irvine Presented by: Amir Haghighat

23. Overview Motivation Architecture Results Conclusion Future Work

24. Introduction and Motivation += Virtual WorldPhysical World or

25. Required Characteristics Fairly accurate (~1 meter) No additional h/w requirement on the part of the user Fairly cheap to deploy

26. Beep Architecture

27. Triangulation where [Xi, Yi, Zi] is the position of the ith sensor. S3 S2 S1 r3 r2 r1

28. Delay Elimination

29. Results

30. Error Estimation

31. Results Accuracy and Precision: 2D: 2 ft (97%) 3D: 3 ft (95%)

32. Beep Performance in Noisy Environment QuietNoisy Beep in noisy environment: 2 feet 90% of time, given the location's distance was not greater than ~18 feet from any 3 sensors (1 sensor per ~160 ft 2 = 15 m 2 )

33. BeepBeep Architecture

34. BeepBeep Performance in Noisy Environment QuietNoisy BeepBeep in noisy environment: 2 feet 80% of time, given the location's distance was not greater than ~15 feet from any 3 sensors (1 sensor per ~110 ft2 =10 m2)

35. Related Work UCLA Pros: Accurate, mainly targeting wireless sensor networks Cons: CPU clocks have to be synched, data is processed offline, no absolute locations

36. Conclusion Fairly accurate (2 ft, 97% of time) No additional h/w requirement on the part of the user (virtually all roaming devices have speakers, WLAN compatibility?) Fairly cheap to deploy (10,000 sq. ft => ~ $5000 at $100 per sensor module)

37. Future Work Eliminate the need for 802.11 on the part of the user Test in an authentic environment (UCI bookstore?) HCI issues Accuracy in presence of authentic noise Less annoying sound than a monotone 4000 Hz

38. GPS-Less Low-Cost Outdoor Localization for Small Devices, UCLA, 2000 Node localizes itself as the centroid of the reference points, from which it can receive beacon signals (proximity-based) Beacon signals are assumed to overlap in space, not in time Location of a node is estimated, using the locations of k reference points whose beacon signals are received X est = (X i1 + X i2 + … + X ik ) / k Y est = (Y i1 + Y i2 + … + Y ik ) / k

39. APS (Ad-Hoc Positioning System), Rutgers, 2001 Each beacon broadcasts a packet with its location and a hop count, initialized to one. The hop-count is incremented by each node as the packet is forwarded. Each node maintains a table of minimum hop-count distances to each beacon

40. APS (Ad-Hoc Positioning System), Rutgers, 2001 A beacon can use the absolute location of another beacon along with the minimum hop count to that beacon to calculate the average distance per hop. The beacon broadcasts the average distance per hop, which is forwarded to all nodes. Individual nodes use the average distance per hop, along with the hop count to known beacons, to calculate their local position using lateration Positioning node within 1/3 radio range in dense networks

41. Project Overview Chris Karlof and David Wagner, "Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures", Elsevier's AdHoc Networks Journal, Special Issue on Sensor Network Applications and Protocols, September 2003.Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures Sybil attack HELLO flood attack Karlok and Wagner explore potential attacks on sensor networks and their countermeasures I plan to work on adversary node localization Absolute or relative position Proximity or RF signal attenuation characteristics Kalman filter for tracking