Location Without GPS

1. Location Without GPS John KrummMicrosoft ResearchRedmond, 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 • LOCADIO – Wi-Fi triangulation • NearMe – Wi-Fi proximity • RightSPOT – FM radio triangulation • TempIO – Inside/outside from temperature

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

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

10. LOCADIO - Constraints Make the client as smart as possible to reduce calibration effort No passing through walls No speeding We know when you move

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

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

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

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

15. Download from http://research.microsoft.com/~jckrumm/NearMe.htm d12 = g(s1, s2) Location vs. Proximity x1 = (x,y) location x2 = (x,y) location d12 = f(x1, x2) s1 = measured signals s2 = measured signals

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

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

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

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

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from http://research.microsoft.com/~jckrumm/NearMe.htm NearMe Client -- Query Adjustable “Look back” time to filter outdated reports

21. Download from http://research.microsoft.com/~jckrumm/NearMe.htm Register as thing Report signal strengths Query for things NearMe Client – Nearby Things

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

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

24. NearMe Demo

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

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

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 Redmond: KPLU < KMTT < KMPS Bellevue: KMTT < KPLU < KMPS Issaquah: KMTT < KMPS < KPLU … Any monotonically increasing function of signal strength preserves ranking Measured Power N radio stations → N! possible rankings • A B C • A C B • B A C • B C A • C A B • C B A A B C Input Power • Each watch scales signal strengths differently • Impractical to calibrate every watch

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 • LOCADIO – Wi-Fi triangulation • NearMe – Wi-Fi proximity • RightSPOT – FM radio triangulation • TempIO – Inside/outside from temperature

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

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

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

37. The End