1. Localization

2. 2 Location Source of wireless signals – Wireless emitter Location of a mobile device – Some devices, e.g., cell phones, are a proxy of a person’s location Used to help derive the context and activity information – Location based services – Privacy problems

3. 3 Location Well studied topic (3,000+ PhD theses??) Application dependent Research areas – Technology – Algorithms and data analysis – Visualization – Evaluation

4. 4 Representing Location Information Absolute – Geographic coordinates (Lat: 33.98333, Long: -86.22444) Relative – 1 block north of the main building Symbolic – High-level description – Home, bedroom, work

5. 5 Some outdoor applications Car Navigation Child tracking Bus view E-911

6. 6 Some indoor applications Elder care

7. 7 No one size fits all! Accurate Low-cost Easy-to-deploy Ubiquitous Application needs determine technology

8. Lots of technologies! 8 Ultrasonic time of flight E-911 Stereo camera Ad hoc signal strength GPS Physical contact WiFi Beacons Infrared proximity Laser range-finding VHF Omni Ranging Array microphone Floor pressure Ultrasound

9. Wireless Technologies for Localization NameEffective RangeProsCons GSM35kmLong rangeVery low accuracy LTE30km-100km Wi-Fi50m-100mReadily available; Medium range Low accuracy Ultra Wideband70mHigh accuracyHigh cost Bluetooth10mReadily Available; Medium accuracy Short range Ultrasound6-9mHigh accuracyHigh cost, not scalable RFID & IR1mModerate to high accuracy Short range, Line-Of-Sight (LOS) NFC<4cmHigh accuracyVery short range 9

10. Localization Techniques Range-based algorithms Range-free algorithms Fingerprinting 10

11. Range Based Algorithms Rely on the distance (angle) measurement between nodes to estimate the target location Approaches – Proximity – Lateration – Hyperbolic Lateration – Angulation Distance estimates – Time of Flight – Signal Strength Attenuation 11

12. Approach: Proximity Simplest positioning technique Closeness to a reference point Based on loudness, physical contact, etc 12

13. Approach: Lateration Measure distance between device and reference points 3 reference points needed for 2D and 4 for 3D 13

14. Approach: Hyperbolic Lateration Time difference of arrival (TDOA) Signal restricted to a hyperbola 14

15. Approach: Angulation Angle of the signals Directional antennas are usually needed 15

16. Distance Estimation Multiple the radio signal velocity and the travel time – Time of arrival (TOA) – Time difference of arrival (TDOA) Compute the attenuation of the emitted signal strength – RSSI Problem: Multipath fading 16

17. Distance Estimation: TOA Distance – Based on one signal’s travelling time from target to measuring unit – d = v radio * t radio Requirement – Transmitters and receivers should be precisely synchronized – Timestamp must be labeled in the transmitting signal 17

18. Distance Estimation: TDOA Distance – Based on time signals’ travelling time from target to measuring unit – d = v radio * v sound * (t radio - t sound ) / (v radio – v sound )) Requirement – Transmitters and receivers should be precisely synchronized – Timestamp must be labeled in the transmitting signal – Line-Of-Sight (LOS) channel 18

19. Distance Estimation: RSSI Distance – Based on radio propagation model – Requirement – Path loss exponent η for a given environment is known 19

20. Range Free Algorithms Rely on target object’s proximity to anchor beacons with known positions – Neighborhood: single/multiple closest BS – Hop-count: anchor broadcast beacons containing its location and hop-count – Area estimation: 20

21. Fingerprinting Mapping solution Address problems with multipath Better than modeling complex RF propagation pattern 21

22. Fingerprinting: Steps Step1 – Use war-driving to build up location fingerprints (i.e. location coordinates + respective RSSI from nearby base stations) Step2 – Match online measurements with the closest a priori location fingerprints 22

23. Fingerprinting: Example SSID (Name)BSSID (MAC address)Signal Strength (RSSI) linksys00:0F:66:2A:61:0018 starbucks00:0F:C8:00:15:1315 newark wifi00:06:25:98:7A:0C23

24. Fingerprinting: Features Easier than modeling Requires a dense site survey Usually better for symbolic localization Spatial differentiability Temporal stability 24

25. Summary of Localization Techniques Measurement Scheme AccuracySpecial Requirement Range-basedTOAModerateSynchronization, dense beacons TDOAHighSynchronization, LOS, dense beacons AOAHighDirectional antenna RSSIModerateNo Range-freeNeighborhoodLowNo Area estimationModerateDense Beacons Hop countModerateDense Beacons FingerprintingRSSIHighNo 25

26. Localization Systems Distinguished by their underlying signaling system – IR, RF, Ultrasonic, Vision, Audio, etc [13] 26

27. GPS Use 24 satellites TDOA Hyperbolic lateration Civilian GPS – L1 (1575 MHZ) 10 meter acc. 27

28. Active Bat Ultrasonic Time of flight of ultrasonic pings 3cm resolution 28

29. Cricket Similar to Active Bat Decentralized compared to Active Bat 29

30. Cricket vs Active Bat Privacy preserving Scaling Client costs Active Bat Cricket 30

31. 31 RADAR WiFi-based localization Reduce need for new infrastructure Fingerprinting 31

32. 32 Place Lab “Beacons in the wild” – WiFi, Bluetooth, GSM, etc Community authored databases API for a variety of platforms RightSPOT (MSR) – FM towers

33. 33 Computer Vision Leverage existing infrastructure Requires significant communication and computational resources CCTV

34. Performance Metrics Accuracy – Mean distance error (RMSE) Precision – Variation in accuracy over many trials (CDF of RMSE) Robustness – Performance when signals are incomplete Cost – Hardware, energy 34

35. Performance Evaluation System/ Solution Wireless Technologies AccuracyPrecisionRobustnessCost Active Badge [1] IR3cm90%PoorLow Cricket[2]Ultrasound5cm90%PoorMedium BeepBeep [3]Sound4cm95%PoorHigh Virtual Compass [4] Bluetooth+ WiFi RSSI 3.19m90%GoodMedium APIT [5]WiFi RSSI0.4 * radio range MediumLow DV-Hop [6]WiFi RSSI3.5m90%MediumLow 35

36. Performance Evaluation System/ Solution Wireless Technologies AccuracyPrecisionRobustnessCost Centroid [7]WiFi RSSI3.5m90%GoodLow Amorphous [8]WiFi RSSI0.2* radio range MediumLow RADAR [9]WiFi RSSI5.9m95%GoodLow Horus [10]Bluetooth+ WiFi RSSI 2.1m90%GoodLow SurroudSense [11] WiFi RSSI90%N/AGoodHigh Ekahau [12]WiFi RSSI2m50%GoodLow 36

37. E-V Loc: Goal Find a specific person’s accurate location based on his electronic identifier and visual image - Publication: Boying Zhang, Jin Teng, Junda Zhu, Xinfeng Li, Dong Xuan, and Yuan F. Zheng, EV-Loc: Integrating Electronic and Visual Signals for Accurate Localization, to appear in ACM MobiHoc’12. 37

38. E-V Loc: Problem Formulation Input: a target object’s electronic identifier EID*, a set (in a short time span) of E Frames with clear EIDs and the corresponding V Frames with possibly vague VIDs Output: the target object’s accurate position together with its visual appearance VID* 38

39. E-V Loc: Work Flow 39 Need more signal samples?

40. E-V Loc: Nature of Our Solution E-V matching – Uses electronic and visual signals as target object’s location descriptors in E frames and V frames – Matches the corresponding E and V location descriptors using Hungarian algorithm 40

41. 41 E-V Loc: Localizing with Distinct VIDs Best match problem between EIDs and VDs EIDs VIDs

42. 42 E-V Loc: Incremental Hungarian algorithm Find the best match between the EIDs and VIDs in each pair of E and V frame Iteratively perform the matching until a threshold is satisfied The threshold is derived based on the variance model of EIDs and VIDs

43. 43 E-V Loc: Localizing with Indistinct VIDs Multi-dimensional best match problem Between EIDs and VIDs Among VIDs

44. 44 E-V Loc: Two-dimensional Hungarian Algorithm Finding correspondence between different VIDs in neighboring frames Based on the correspondence, generating a consistent set of VIDs in all frames Using incremental Hungarian algorithm to perform the match

45. References 1.Roy Want, Andy Hopper, Veronica Falcao, and Jonathan Gibbons. The active badge location system. ACM Transactions on Information Systems, 10(1):91– 102, 1992. 2.N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket locationsupportsystem. In Proc. of ACM MobiCom, pages 32–43, 2000. 3.C. Peng, G. Shen, Y. Zhang, Y. Li, and K. Tan. BeepBeep: ahigh accuracy acoustic ranging system using COTS mobiledevices. In ACM SenSys, pages 1– 14, 2007. 4.N. Banerjee, S. Agarwal, P. Bahl, R. Chandra, A. Wolman, and M. Corner.Virtual compass: relative positioning to sense mobile social interactions. InPervasive, 2010. 5.T. He, C. Huang, B. Blum, J. Stankovic, and T. Abdelzaher. Range-free localizationschemes for large scale sensor networks. In Proc. of ACM MobiCom,pages 81–95, 2003.

46. References 6.D. Niculescu and B. Nath. DV based positioning in ad hoc networks. Journalof Telecom. Systems, 2003. 7.N. Bulusu, J. Heidemann, and D. Estrin. Gps-less low cost outdoor localizationfor very small devices. IEEE Personal Communications Magazine, 7(5):28– 34,October 2000. 8.R. Nagpal. Organizing a global coordinate system from local information on anamorphous computer. In A.I. Memo 1666. MIT A.I. Laboratory, August 1999. 9.P. Bahl and V. N. Padmanabhan. RADAR: an in-building rf-based user locationand tracking system. In Proc. of IEEE INFOCOM, March 2000. 10.M. Youssef and A. Agrawala. The Horus WLAN location determination system.In Proc. of ACM MobiSys, June 2005. 11.M. Azizyan, I. Constandache, and R. Roy Choudhury. Surroundsense: mobilephone localization via ambience fingerprinting. In Proc. of ACM MobiCom,2009.

47. References 12.http://www.ekahau.com/http://www.ekahau.com/ 13.Shwetak N. Patel, Location in Pervasive Computing, University of Washington.