1. April 20, 2008Emmett Nicholas ECE 256 1 Drive-by Localization of Roadside WiFi Networks Anand Prabhu Subramanian, Pralhad Deshpande, Jie Gao, Samir R. Das Accepted in INFOCOM 2008, Phoenix, Arizona, April 2008

2. April 20, 2008Emmett Nicholas ECE 256 2 Motivation Learn about the nature of WiFi networks –Density, connectivity, interference properties –LOCATION of the APs –Provide datasets for research on Internet topology Localization of infrastructure nodes (APs)

3. April 20, 2008Emmett Nicholas ECE 256 3 Existing Technologies GPS –Not available on most wireless clients today RADAR –Uses WIFI fingerprints for indoor localization VORBA –Rotating directional antennas are used in APs –Signal strength and angle of arrival (AoA) used to localize clients indoors War-driving databases –Locations where APs are heard with a sniffer MobiSteer –Steerable beam directional antenna with a WiFi client mounted on a moving car

4. April 20, 2008Emmett Nicholas ECE 256 4 Drive-by Localization (DriveByLoc) Use MobiSteer –Gather frames from roadside APs on different directional beams –Estimate the AoA of the frames –Many samples are collected from different locations Passive approach –Based on “sniffing” –APs are unaware of localization effort

5. April 20, 2008Emmett Nicholas ECE 256 5 Hardware/Software Setup Multi-beam 2.4 GHz antenna –1 omnidirectional beam –16 directional beams 45⁰ half-power beam-width Rotated 22.5⁰ with respect to adjacent beam –Electronically steerable GPS receiver Each received frame is logged with the tuple:

6. April 20, 2008Emmett Nicholas ECE 256 6 Experimental Scenarios Parking lot (2 APs) Apartment complex (17 APs) Office building (2 APs)

7. April 20, 2008Emmett Nicholas ECE 256 7 Data Collection Ideally, measurements for each AP are taken on all beams at many points –Beam with highest SNR is pointing towards AP Complications… –Each channel/beam combination takes ≈ 100ms –Determining orientation –Non-zero beamwidth –Reflections

8. April 20, 2008Emmett Nicholas ECE 256 8 Localization Algorithm Estimate AoA of frames from a given AP at each measurement point –Average SNR for frames on each directional beam Beam with strongest average SNR is expected to point directly to AP –Orientation information & strongest beam used to position AP –Sum-square of angular error from all strongest beam directions is minimized

9. April 20, 2008Emmett Nicholas ECE 256 9 Non-zero Beamwidth

10. April 20, 2008Emmett Nicholas ECE 256 10 Reflections

11. April 20, 2008Emmett Nicholas ECE 256 11 Understanding Reflections Parking lotOffice building

12. April 20, 2008Emmett Nicholas ECE 256 12 Understanding Reflections Parking lotOffice building Interesting observation: CLUSTERING

13. April 20, 2008Emmett Nicholas ECE 256 13 Understanding Reflections Parking lotOffice building New approach… 1.Use the k-means algorithm to group the measurement points into k clusters 2.Determine which one these k images is the real AP

14. April 20, 2008Emmett Nicholas ECE 256 14 Modeling Reflections by k-Means Clustering For any given value of k, assume L 1,…,L k are the k locations of the AP (real and the images) –L i ’s are chosen randomly within “feasible region” –Each measurement mapped to some L i that provides minimum angular error

15. April 20, 2008Emmett Nicholas ECE 256 15 Modeling Reflections by k-Means Clustering 1.Compute a point for each cluster, Ci, in the feasible region that minimizes intra-cluster sum-square of angular errors 2.C i ’s become new L i ’s 3.Go to Step 1.

16. April 20, 2008Emmett Nicholas ECE 256 16 Choosing Real AP Location from k Images Impossible to know for sure But a certain heuristic helps: –Each measurement ranks k images based on distance to itself –“The nearest image is ranked 1 st and the next is ranked 2 nd and so on.” –Choose image with least sum of ranks

17. April 20, 2008Emmett Nicholas ECE 256 17 Learning k for Clustering Use the idea from the G-means algorithm Start with k=1, and successively increment k –Perform k-means clustering for each k –Check whether error values in each cluster satisfy statistical test for normality If YES, stop. If NO, increment k and repeat.

18. April 20, 2008Emmett Nicholas ECE 256 18 Performance Evaluation

19. April 20, 2008Emmett Nicholas ECE 256 19 Benefit of Using Directional Antennas and AOA “DrivebyLoc is about an order of magnitude better than trilateration”

20. April 20, 2008Emmett Nicholas ECE 256 20 Benefit of Modeling Reflection Using Clustering “Overall it should be recommended that DrivebyLoc be used with modeling beamwidth”

21. April 20, 2008Emmett Nicholas ECE 256 21 Impact of GPS Accuracy

22. April 20, 2008Emmett Nicholas ECE 256 22 Impact of Car Speed

23. April 20, 2008Emmett Nicholas ECE 256 23 Conclusions Contributions –Completely passive –Realization that signal reflections can cause significant localization errors Development of clustering method to solve this problem Enables accurate WiFi map of urban APs with minimum effort What about 3D?

24. April 20, 2008Emmett Nicholas ECE 256 24 Thank You