Precise distributed localization algorithms for wireless networks

We propose reliable and precise distributed localization algorithms for wireless networks, iterative multidimensional scaling (IT-MDS) and simulated annealing multidimensional scaling (SA-MDS). We use only radio communication constraints to infer a node's distances, and uniquely combine a multidimensional scaling algorithm (MDS) with a lateration algorithm. We analytically establish the upperbound on the estimation error. The proposed techniques can estimate all nodes' positions, even with limited and imprecise network knowledge. Analysis and test runs show that the proposed methods are independent of the topology randomness and the range measurement errors. We attempt to bound the localization imprecision. Simulation results yield an average estimation error of about 25% of radio transmission range. The highest estimation error observed is 40% radio range for most networks.

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