Locating in Fingerprint Space: Wireless Indoor localization with Little Human Intervention

1. Locating in Fingerprint Space:Wireless Indoor localization with Little Human Intervention Zheng Yang, Chenshu Wu, and Yunhao Liu MobiCom 2012 - Sowhat 2012.08.20

2. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

3. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

4. Motivation • RSSI fingerprinting-based localization • Site survey • Time-consuming • Labor-intensive • Vulnerable to environmental dynamics • Inevitable

5. Objective

6. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

7. LiFS, System Architecture RSSI + Distance Geographical dist. ≠ Walking dist.

8. Multidimensional Scaling (MDS) • Information visualization for exploring similarities/dissimilarities in data

9. Stress-free Floor Plan • Geographical distance ≠ Walking distance,Ground-truth floor plan –conflict with measured distance • Sample grids in a floor plan (grid length l = 2m) • Distance matrix D = [dij],dij = walking distance between point i and j • Stress-free floor plan – 2D & 3D MDS

10. Fingerprint Space – Fingerprint & Distance Measurement • Fingerprints and distance collection • Record while walking • Footsteps every consecutive steps by accelerometer • Set of fingerprints, F = {fi, i = 1~n}Distance(footsteps) matrix, D’=[d’ij] • Pre-processing • Merge similar fingerprints (δij<ε) • Accelerometer readingTwice integration  Distance: NoiceLocal variance threshold method  Step count • Stride lengths vary?  MDS tolerate measurement errors

11. Fingerprint Space – Fingerprint Space Construction • Adequate fingerprints & distance • 10x sample locations in stress-free floor plan • First several days for training • d’ijunavailable  d’ij= d’ik + d’kj • Shortest path  update D’ • all-pairs of fingerprints • Floyd-Warshall algorithm • MDS  Fingerprint space 2D & 3D

12. Mapping –Corridor & Room Recognition • Corridor recognition (Fc) • Higher prob. on a randomly chosen shortest path • Minimum spanning tree • Betweenness • Watershed • Size(corridor) / Size(all) • Large gap of betweenness values • Room recognition (FRi) • k-means algorithm(k = number of rooms) Classify fingerprints into the corridor or rooms

13. Mapping –Reference Point • Fingerprints collected near “doors” • PD = {p1, p2, …, pk}, stress-free floor planFD , fingerprint space • distance matrix D and D’  l = (lp1, lp2, …, lp k-1)l’ = (lf1, l’f2, …, l’f k-1)cosine similarity Map near-door fingerprintsto real locations (FD→ PD) Map rooms to rooms Near-door fingerprints, FD,labeled with real locations

14. Mapping –Space Transformation • Floor-level transformation • Stress-free floor plan ≠ Fingerprint space∵ translation, rotation, reflection • Transform matrix,xi = coordinate of fi ∈ FDyi= coordinate of pi ∈PD • For fingerprint with coordinate xreal location = sample location closest to Ax + B • Room-level transformation • Room by room • Doors and room corners as reference point • Transformation matrix

15. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

16. Hardware and Environment • 2 Google Nexus S phones • Typical office building covering 1600m2 • 16 rooms,5 large – 142m2, 7 small, 4 inaccessible • 26 Aps, 15 are with known location • 2m x 2m grids, 292 sample locations

17. Experiment Design • 5 hours with 4 volunteers • Fingerprints recording – every 4~5 steps (2~3m) • Accelerometer – work in different frequency based on detecting movement • 600 user traces, with 16498 fingerprints • Corridor, >500 pathsSmall rooms, >5 pathsLarge rooms, >10 paths • Half of data used for training,half …………………... in operating phase

18. Threshold Value of Fingerprint Dissimilarity

19. Step Count • 5 ~ 200 footsteps • Error rate = 2% in number of detected steps • Accumulative error of long path • Unobvious performance drop • ∵ only use inter-fingerprint step counts

20. Fingerprint Space • 795 fingerprints when ε = 30

21. Corridor Recognition • Refining • Perform MST iteratively • Sift low betweenness • Until MST forms a single line

22. Room Recognition

23. Reference Point Mapping

24. Point Mapping • 96 percentile < 4m • Average mapping error = 1.33m

25. Localization Error • Emulate 8249 queries using real data on LiFS • Location error • Average,LiFS = 5.88mRADAR = 3.42m • Percentile of LiFS80 < 9m / 60 < 6m • Caused bysymmetric structure • Fairly reasonable! • Room error = 10.91%

26. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

27. Discussion • Global reference point • Last reported GPS locationLocations of APsSimilar surrounding sound signature… • Could be added in LiFS for more robust mapping • Key for symmetric floor plans / multi-floor fuildings • Large open environment

28. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

29. Conclusion • LiFS • Spatial relation of RSSI fingerprints + Floor plan • Low human cost • Comments • Clear architecture • Not specific descriptions in evaluation

30. Thanks for Listening ~