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Distributed localiza tion in wireless sensor networks

Distributed localiza tion in wireless sensor networks. Koen Langendoen Niels Reijers Delft University of Technology The Netherlands. Technology trend. S mall integrated devices Smaller, cheaper, more powerful PDAs, mobile phones Many opportunities, and research areas Power management

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Distributed localiza tion in wireless sensor networks

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  1. Distributed localization in wireless sensor networks Koen Langendoen Niels Reijers Delft University of Technology The Netherlands

  2. Technology trend • Small integrated devices • Smaller, cheaper, more powerful • PDAs, mobile phones • Many opportunities, and research areas • Power management • Distributed algorithms

  3. Wireless sensor networks • Wireless sensor node • power supply • sensors • embedded processor • wireless link • Many, cheap sensors • wireless  easy to install • intelligent  collaboration • low-power  long lifetime

  4. Possible applications • Fire rescue • breadcrumbs • exit path • hazard detection • Environmental monitoring • detecting forest fires • Monitoring bulk goods (potatoes) • mix sensors with goods • temperature, humidity

  5. Required technologies • Efficient data routing • ad-hoc network • one or more ‘datasinks’ • In-network data processing • large amounts of raw data • limited power and bandwidth • Node localization

  6. Ad-hoc localization • Many nodes (> 100) • NO infrastructure • NO central processing • Sparse anchor nodes • known position • Other nodesdetermine position using this data • Distance measurement

  7. Ad-hoc localization • 2D, static node positions • Several different algorithms have been proposed • 3 will be compared • Simulations on DAS2 supercomputer

  8. Main result no ‘one size fits all’ Best algorithm depends on: • error in range measurement (range variance) • connectivity (number of neighbours) • network topology • node capabilities • application requirements

  9. Three-phase approach • Determine distance to anchor nodes (communication) • Establish position estimates (computation) • Iteratively refine positions using additional range measurements (both)

  10. Phase 1: Distance to anchor • Three algorithms • Sum-dist [Savvides et al.] • DV-Hop [Niculescu et al., Savarese et al.] • Euclidean [Niculescu et al.] • anchors flood network with their known position

  11. 6 6 4 5 5 A: 5 B: 6+4 = 10 C: 5+6+4 = 15 Phase 1:Sum-dist Anchors • flood network with known position Nodes • add hop distances • require range measurement B C A

  12. A-B: 12 3 2 4 1 2 3 1 2 1 4 1 2 2 3 Phase 1: DV-hop Anchors • flood network with known position • flood network with avg hop distance Nodes • count #hops to anchors • multiply with avg hop distance 3 hops B avg hop: 4 C A

  13. Phase 1:Euclidean Anchors • flood network with known position Nodes • determine distance by • range measurement • geometric calculation • require range measurement B C A

  14. A-G = 8 Phase 1:Euclidean (2) D • Wanted: Distance A-G G E Using AEGF: A-G = 8 ...or 3 F Using AEGD: A-G = 8 ...or 0.5 A

  15. Phase 1:Euclidean (3) • Needs high connectivity • Error prone (selecting wrong distance) • Perfect accuracy possible B D G E C F A

  16. Phase 1:Comparison • Range measurement • Very accurate: Euclidean • Reasonable: Sum-dist • None / very bad: DV-hop

  17. Phase 2:Determining position • Two algorithms: • Lateration • very common • local triangulation • solve [Ax=b] • Min-max [Savvides et al.] B C A

  18. Phase 2:Min-max • Using range to anchors to determine a bounding box • Use center of box as position estimate B C A

  19. Comparison: distance error

  20. Comparison: distance bias

  21. A problem with Min-max Very sensitive to anchor placement

  22. Phase 1 + 2 combined

  23. Phase 1 + 2 combined Euclidean: very sensitive to both range variance and connectivity

  24. There is a tradeoff between coverage and error Error and coverage

  25. Matrix

  26. Phases 1 and 2 • Position error usually 30% of the radio range or higher • Range measurements between nodes only used to determine anchor distance • Can we do better?

  27. Phase 3: Iterative refinement • obtain initial position (phases 1 and 2) • broadcast my position • iteratively refine position using: • ranges to direct neighbours • their initial positions

  28. Broadcast new position Phase 3:Iterative refinement • Initial estimate • Receive neighbour positions • Local lateration A

  29. Phase 3: Position error

  30. Phase 3: Coverage

  31. Conclusion • No ‘one size fits all’ • Refinement needs better coverage to be useful • Lots of room for improvement in all phases • Details in Tech Report PDS-2002-03 (http://pds.twi.tudelft.nl/reports/2002/PDS-2002-003)

  32. What is wrong? • Bad topology • identical hop-TERRAIN positions • twins • Error propagation • rapid infection of complete network • – hop – triangulate – hop – triangulate –

  33. Confidence weights • Weight input for triangulation (wAx = wb) • Initialization • anchors 1.0 • twins, identical hops 0 • others 0.1 • Triangulation • large residue 0 • small residue avg of input confidences

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