Robust and Distributed Stochastic Localization in Sensor Networks: Theory and Experimental Results

We present a robust localization system allowing wireless sensor networks to determine the physical location of their nodes. The coverage area is partitioned into regions and we seek to identify the region of a sensor based on observations by stationary clusterheads. Observations (e.g., signal strength) are assumed random. We pose the localization problem as a composite multihypothesis testing problem, develop the requisite theory, and address the problem of optimally placing clusterheads. We show that localization decisions can be distributed by appropriate in-network processing. The approach is validated in a testbed yielding promising results.