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A New Hybrid Wireless Sensor Network Localization System

A New Hybrid Wireless Sensor Network Localization System. Ahmed A. Ahmed, Hongchi Shi , and Yi Shang Department of Computer Science University of Missouri-Columbia Columbia, Missouri, USA. Outline. Introduction Related Work Network Properties Adaptive Localization System (ALS)

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A New Hybrid Wireless Sensor Network Localization System

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  1. A New Hybrid Wireless Sensor Network Localization System Ahmed A. Ahmed, Hongchi Shi, and Yi Shang Department of Computer Science University of Missouri-Columbia Columbia, Missouri, USA

  2. Outline • Introduction • Related Work • Network Properties • Adaptive Localization System (ALS) • Experimental Results • Conclusion ICPS 2006

  3. Introduction • A wireless sensor network is represented as an undirected connected graph with vertices (nodes) V and edges E. • Edges are: • Connectivity information or • Estimated distances to neighbors. • Some of the nodes are anchors (with known positions). • Relative vs. absolute localization. ICPS 2006

  4. Related Work (1/3)Ad-hoc Positioning System (APS)Niculescu et al., GLOBECOM’01 1. Each anchor k • broadcasts its position, • receives the positions of all m anchors, and • computes the shortest-path distance p to each anchor. 2. Each anchor k computes its distance correction value, ck. 3. Each unknown node • computes the corrected shortest-path distances to all anchors, and • multilaterates based on all anchors to determine its position. ICPS 2006

  5. Related Work (2/3)MultiDimensional Scaling (MDS-MAP)Shanget al., MobiHoc’03. 1. Set the range for local maps to Rlm. 2. Compute relative maps for individual nodes within Rlm . • Compute all-pair shortest paths. • Apply MDS to the distance matrix and construct the local maps. 3. Merge the relative maps to form one global map. 4. Given sufficient anchors, transform the relative map to an absolute one. ICPS 2006

  6. Related Work (3/3)SemiDefinite Programming (SDP)Biswas et al., IPSN’04 • The problem is considered in the presence of measurement errors. • By introducing slack variables and then relaxing the problem, it is rewritten as a standard SDP problem. ICPS 2006

  7. NetworkProperties • Network topology • Random uniform (isotropic) • Grid • C-shape (anisotropic) • Average network connectivity • Measurement error • Received Signal Strength Indicator (RSSI) • Time of Arrival (ToA) • Time Difference of Arrival (TDoA) • Anchor ratio • Anchor placement • Random • Outer ICPS 2006

  8. Adaptive Localization System (ALS) • Phase 1: Discover network properties. • Phase 2: Run the three localization methods: APS, MDS, and SDP. • Phase 3: Using the appropriate weights, compute the weighted centroid of the three position estimates. ICPS 2006

  9. SimulationSetup Total # of network instances = 2 X 8 X 3 X 3 X 2 = 288 ICPS 2006

  10. Topologies Isotropic network, 100 nodes Average node connectivity = 14.7 Anisotropic network, 100 nodes Average node connectivity = 14.9 ICPS 2006

  11. Determining the Weights (1/2) • Find the values of the weights that give the minimum localization error under a specific set of network properties. • Train off-line using 30 network instances for every one of a 288-combination set. • Find the values of the weights by solving the constrained linear least-squares problem. • Test on a different set of 30 networks for every combination. ICPS 2006

  12. Determining the Weights (2/2) • For node i, let • xi= [xi yi]T be the true position, • xia= [xia yia]T be the estimated position using APS, • xim= [xim yim]T be the estimated position using MDS, • xis= [xis yis]T be the estimated position using SDP,. • Define the weighted centroid of the three estimates as xic= [xic yic]T where xic = wa xia + wmxim + wsxis yic = wa yia + wmyim + wsyis ICPS 2006

  13. Experimental Results (1/4) ICPS 2006

  14. Experimental Results (2/4) ICPS 2006

  15. Experimental Results (3/4) ICPS 2006

  16. Experimental Results (4/4) ICPS 2006

  17. Conclusion • We have identified 5 network properties that may affect performance. • We present our Adaptive Localization System (ALS) method based on 3 existing algorithms. ALS has 3 phases: • Discover network properties. • Run three localization methods. • Compute a new position estimate that is the weighted centroid of the three estimates. • We use machine learning to compute the values of the weights. • ALS outperforms the individual algorithms under a broad range of networks conditions. • In the future, we will consider • the performance-cost tradeoff in localization. ICPS 2006

  18. Thanks!Questions / Comments ? ICPS 2006

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