1. Location Without GPS John Krumm Microsoft Research Redmond, Washington, USA

2. Seattle, Washington, USA Kyoto, Japan Location

3. Importance of Location Find your way Find nearby things Invoke location-based services –Electronic graffiti, e.g. “There is a better Mexican restaurant 0.2 km north of here.” –List of nearby events Part of context –In lecture hall → cell phone off –At home → use home network

4. IWMMS & Location “Study of Structuring and Recalling Life Log Experience Using Location Information”, Y. Aihara, R. Ueoka, K. Hirota and M. Hirose -- Already using location for activity inference “Active Wearable Vision Sensor: Recognition of Human Activities”, K. Sumi, M. Toda, S. Tsukizawa and T. Matsuyama “Cooperative Dialogue Planning with User and Situation Models via Example-based Training”, I. R. Lane, S. Ueno and T. Kawahara -- Inferring context of user – location is part of context “A Hybrid Dynamical System for Event Segmentation, Learning, and Recognition”, H. Kawashima, K. Tsutsumi and T. Matsuyama “Time-Series Human-Motion Analysis with Kernels derived from Learned Switching Linear Dynamics”, T. Mori, M. Shimosaka, T. Harada and T. Sato -- Apply HDS/SLDS to infer location & mode of transportation & destination?

5. Why Not Use GPS? Does not work indoors Needs view of satellites

6. Location Sensing Hazas, Scott, Krumm, “Location-Aware Computing”, IEEE Computer Magazine, February 2004.

7. Outline Introduction L OCADIO – Wi-Fi triangulation NearMe – Wi-Fi proximity RightSPOT – FM radio triangulation TempIO – Inside/outside from temperature

8. Location from 802.11 with L OCADIO * Mobile device measures signal strengths from Wi-Fi access points Computes its own location Wi-Fi (802.11) access point * Location from Radio with Eric Horvitz

9. L OCADIO – Radio Survey Radio survey to get signal strength as a function of position

10. L OCADIO - Constraints No passing through wallsNo speedingWe know when you move Make the client as smart as possible to reduce calibration effort

11. L OCADIO - Results Hidden Markov model gives median error of 1.53 meters

12. Outline Introduction L OCADIO – Wi-Fi triangulation NearMe – Wi-Fi proximity RightSPOT – FM radio triangulation TempIO – Inside/outside from temperature

13. NearMe conference rooms printers bathroom reception desk people Find people and things nearby Download from http://research.microsoft.com/~jckrumm/NearMe.htm with Ken Hinckley

14. The Basic Idea 802.11 Wi-Fi access point NearMe Proximity Server Download from http://research.microsoft.com/~jckrumm/NearMe.htm

15. Location vs. Proximity s 1 = measured signals s 2 = measured signals x 1 = (x,y) location x 2 = (x,y) location d 12 = f(x 1, x 2 ) d 12 = g(s 1, s 2 ) Download from http://research.microsoft.com/~jckrumm/NearMe.htm

16. NearMe Client Windows XP PocketPC 2003 Requirements: Windows XP WWW access Microsoft.NET Framework Download from http://research.microsoft.com/~jckrumm/NearMe.htm

17. NearMe Client – Test Connections Download from http://research.microsoft.com/~jckrumm/NearMe.htm

18. NearMe Client – Register Register with: Name Email (optional) URL (optional) Expiration interval Download from http://research.microsoft.com/~jckrumm/NearMe.htm

19. NearMe Client – Report Wi-Fi List of detectable Wi-Fi access points Access points used only as beacons Periodic reports for mobility Download from http://research.microsoft.com/~jckrumm/NearMe.htm

20. NearMe Client -- Query Adjustable “Look back” time to filter outdated reports Download from http://research.microsoft.com/~jckrumm/NearMe.htm

21. Register as thing NearMe Client – Nearby Things personelevatorkitchenbathroom conference roomstairsmail roomstitchable device printercafeteriareception deskdemo person Report signal strengths Query for things Download from http://research.microsoft.com/~jckrumm/NearMe.htm

22. Simple Distance Function d = -2.53∙ n ∩ – 2.90∙ ρ s - 22.31 rms error = 14.04 meters ρ s = 0.39 Download from http://research.microsoft.com/~jckrumm/NearMe.htm

23. Access Point Layout 2 1 3 A F B D C E F A B C D E Access point topology in database Recomputed every hour Download from http://research.microsoft.com/~jckrumm/NearMe.htm

24. NearMe Demo

25. Outline Introduction L OCADIO – Wi-Fi triangulation NearMe – Wi-Fi proximity RightSPOT – FM radio triangulation TempIO – Inside/outside from temperature

26. SPOT Watch Location weather traffic diningmovies Commercial FM: transmit new data every ~2 minutes Filter on watch to take what it wants Watch displays “personalized” data with Adel Youssef, Ed Miller, Gerry Cermak, Eric Horvitz

27. Location-Sensitive Features Nice to have Local traffic Nearby movie times Nearby restaurants Need to know location of device …

28. Use FM Radio Signal Strengths Scan signal strengths of 32 FM radio stations at 1 Hz

29. Ranking Approach Each watch scales signal strengths differently Impractical to calibrate every watch Input Power Measured Power ABC Redmond:KPLU < KMTT < KMPS Bellevue:KMTT < KPLU < KMPS Issaquah:KMTT < KMPS < KPLU … Any monotonically increasing function of signal strength preserves ranking N radio stations → N! possible rankings 1.A B C 2.A C B 3.B A C 4.B C A 5.C A B 6.C B A

30. Test Six suburbs and six radio stations 81.7% correct from 8 radio stations

31. Avoid Manual Training Seattle KMPS 94.1 MHz KSER 90.7 MHz

32. Classify Into Grid Cell Find location in grid Use predicted signal strengths to avoid manual training ≈ 8 kilometers average error Summer intern Adel Youssef, U. Maryland

33. Outline Introduction L OCADIO – Wi-Fi triangulation NearMe – Wi-Fi proximity RightSPOT – FM radio triangulation TempIO – Inside/outside from temperature

34. TempIO – Inside/Outside Classification Suunto X9 – GPS, altimeter, thermometer Suunto N3 – SPOT watch, knows outside temperature, location Are you inside or outside? Turn off GPS if inside to save batteries Metadata for digital photos Higher-level context reasoning Bayes Net with Ramaswamy Hariharan

35. World Weather Stations 6509 weather stations → http://weather.noaa.gov/weather/metar.shtml → our web servicehttp://weather.noaa.gov/weather/metar.shtml

36. Inside/Outside from Temperature From hourly temperature data in five US cities, 2003 Average correct 81% Kyoto

37. The End