Position Estimation for Wireless Sensor Networks

1. Position Estimation for Wireless Sensor Networks K.-F. Simon Wong Hong Kong University of Science and Technology

2. Outline • Introduction • Position estimation scheme • ISOMAP • Distributed Algorithm • Applications • Position-Based Routing • Location-Identifying Service • Illustrative Results • Conclusion

3. Introduction

4. Background • Ad hoc network • High mobility, high power nodes and moderate network size. • Wireless sensor networks (WSNs) • Low mobility, low power nodes and large size (typically more than 50 nodes). • We focus on WSNs in this work.

5. Position Estimation in WSNs • Hot topic • Position-based routing • Route according to the node’s location instead of IDs. • Location-based services • Identify the location at which sensor reading originate. • Enclosed environment, such as car park, hospital, theme park and so on.

6. Previous Work • Two approaches for location-identifying • Approaches based on precise measurement. • Landmark-based approaches.

7. Approaches Based on Precise Measurement • GPS, RADAR, APS and so on. • Expensive hardware. • Power inefficient. • Good for Ad hoc networks, but not suit for WSNs.

8. Landmarks-based Approaches • Centroid algorithm, APIT, HS/GHoST, DV-HOP and so on. • Centralized algorithm. • Usually require high powered landmarks. • Bandwidth-inefficient flooding. • Good approaches, if decentralize the algorithm, and avoid flooding.

9. Our Contribution • No expansive hardware • Reduction in implementing cost. • Less power consumption. • Distributed Algorithm • Collects information from certain number (C) of neighbors (C = 30 in our experiment). • Each node estimates its own coordinates. • Landmark-free • Landmarks are optional.

10. Position Estimation Scheme

11. Quantized Distance • Measuring rough distances between one-hop neighbors by power controlling. 2 4 5 1 3 CNV • Construct close-neighbor vector (CNV) for information exchanges. • Inf • 2 • 3 • 2 Host ID Distance levels

12. Collecting CNV • Collecting CNV to construct distance matrix.

13. ISOMAP • Given: a matrix of quantized distance of a number of nodes • Finding: the corresponding coordinates that fits the matrix and minimize error. 0 4 inf 3 4 0 2 1 inf 2 0 3 3 1 3 0 0 0 0 3 0 4 0 6

14. Distributed Algorithm • Clearly, centralized algorithm. • Challenging to be distributed. • Demonstrates the idea in the following slides.

15. = bootstrap node Every node obtains its own CNV at the beginning. = normal users

16. First iteration The bootstrap asks the C neighbors to send CNV, and runs isomap.

17. First iteration = Coordinates computed The bootstrap sends the computed coordinates to the C neighbors. = Not computed yet

18. Second iteration Each node collects CNV from the C closest neighbors.

19. If there is L neighbors already computed new coordinates, perform isomap to compute its OWN coordinate. (L is typically 10 for C = 30) Second iteration

20. The computed nodes diffuses outwards. Third iteration

21. Finally done! Nth iteration

22. Applications

23. Position Based Routing • Plenty of existing algorithms. • Most of them depend on GPS. • We are not proposing a new one and only gives important location information for these algorithms. • A simple algorithm is used • Greedy forwarding.

24. Location-based Service • Relative location in position-based routing. • Landmarks can fix the rotation/reflection • No high powered landmarks. • No landmarks flooding. • Small number (around 10).

25. Illustrative Results

26. Relative Coordinates

27. Global Coordinates

28. Routing Effect:Low Failure rate

29. Positioning Effect:Lower Angular Error

30. Positioning Effect:Low Distance Error

31. Conclusion

32. Conclusion • Presented a position estimation system in WSNs. • Focus in two applications: • Position-based routing. • Location-based services. • Simulation results are shown to illustrate the performance.