1. ACCURACY CHARACTERIZATION FOR METROPOLITAN-SCALE WI-FI LOCALIZATION Presented by Jack Li March 5, 2009

2. Acknowledgments  Authors  Yu-Chung Cheng  Yatin Chawathe  Anthony LaMarca  John Krumm  Some text and pictures taken from:  http://sysnet.ucsd.edu/~ycheng/papers/mobisys 05placelab.ppt

3. Introduction  Many new applications need/can benefit from context-awareness  Maps, directions on your phone  Social applications (Twitter)  Location-enhanced content (searching for nearby restaurants)  Emergency services (911)  Problem  Need to maximize coverage  Work wherever devices are taken  Low calibration overhead  Must scale with coverage  Low cost for user  Commodity devices (e.g. cell phones, music players)

4. Existing solutions  GPS  high coverage and accuracy (<10 m)  Does not work indoors or in urban canyons  Not as prevalent  ActiveCampus  Access point locations are known  RADAR  Median error of 2.94 meters  Many hours of installation

5. Proposed Solution  Wi-Fi is everywhere  No new infrastructure  Low cost for users  Can be used indoors as well as outdoors (RADAR)  “war driving” already builds AP maps  Two phases  Training phase  Positioning phase Manhattan (Courtesy of Wigle.net)

6. Metrics for Positioning  Signal strength  For a given location, SSs are relatively stable  Response rate  Collect all Wi-Fi scans that are at the same distance from an access point  Compute the fraction of times that this AP is heard in that collection of scans

7. Positioning Algorithms  Centroid  Combine all the readings for a single access point and take the arithmetic mean of the positions reported to estimate the AP’s geographic location  User is placed at the average position of all heard APs AP 1 AP 2 AP 3 USER

8. Positioning Algorithms  Fingerprinting  Each scan is a unique “fingerprint” that contains the GPS location and the associated APs for that location  Compute the k-nearest-neighbor in signal space   k = 4 provides good accuracy  Match fingerprints that have at most p=2 different APs between the fingerprint in the radio map and observed scan

9. Positioning Algorithms  Ranking  Used to account for variations in signal strength that might be due to different devices/manufacturers  Compare a list of access points sorted by SS instead of absolute SS  (SS A, SS B, SS C ) = (-20, -90, -40) converted into (R A, R B, R C ) = (1, 3, 2)  Compare relative rankings using Spearman rank-order correlation coefficient [-1, 1]

10. Positioning Algorithms  Particle Filters  Probabilistic approximation algorithm  Location estimate of a user at time t using collection of weighted particles  Each particle is a distinct hypothesis about user’s current location and each particle has an associated weight  Sensor model How likely it is that a given set of APs would be observed at a given location  Motion model Move the particles’ locations in a manner that approximates the motion of the user  SS and response rate sensor model built Each scan, look up the response rate or the probability of seeing the measured SS based on the distance between the particle and the estimated AP location in the radio map

11. Methodology  Training phase  Collect AP beacons by “war driving” with Wi-Fi enabled laptop and attached GPS unit  Scans every second – each scan consists of list of APs and the associated GPS coordinate  1 km 2 neighborhood covered in 1 hr  Build a radio map out of the AP traces – many different ways to build map  Positioning phase  Use radio map to position the user  Compare the estimated position with the actual GPS location

12. Downtown vs. Urban Residential vs. Suburban Downtown (Seattle) Urban Residential (Ravenna) Suburban (Kirkland)

13. Results

15. Effect of #APs/scan and AP Turnovers

16. Effect of GPS noise and density of mapping data  Centroid cancels out some GPS noise  Particle filter techniques rely on empirical models built using the same radio map  25 mph driving speed with 1 scan/s is approximately 1 scan/10 meters  More “war drives” won’t help

17. Summary  Wi-Fi-based location with low calibration overhead  1 city neighborhood in 1 hour  Positioning accuracy depends mostly on AP density  Urban 13~20m, Suburban ~40m  Dense AP records get better accuracy  In urban area, simple (Centroid) algorithm yields same accuracy as other complex ones  AP turnovers & low training data density do not degrade accuracy significantly  Low calibration overhead  Noise in GPS only affects fingerprint algorithms

18. Issues  Some APs may be set to not broadcast – use passive scanning to detect network traffic  Did not perform any indoor traces – Place Lab cannot estimate room-level accuracy  Orinoco chipset used in all experiments – can report different SS values for the same AP at the same location

19. Q&A  Thanks for listening