Statistical Location Detection

Statistical Location Detection

Saikat Ray (University of Bridgeport, USA), Wei Lai (Boston University, USA), Dong Guo (Boston University, USA) and Ioannis Ch. Paschalidis (Boston University, USA)
DOI: 10.4018/978-1-60566-396-8.ch009


The authors present a unified stochastic localization approach that allows a wireless sensor network to determine the physical locations of its nodes with moderate resolution, especially indoors. The area covered by the wireless sensor network is partitioned into regions; the localization algorithm identifies the region where a given sensor resides. The localization is performed using an infrastructure of stationary clusterheads that receive beacon packets periodically transmitted by the given sensor. The localization algorithm exploits the statistical characteristics of the beacon signal and treats the localization problem as a multi-hypothesis testing problem. The authors provide an asymptotic performance guarantee for the system and use this metric to determine the optimal placement of the infrastructure nodes. The placement problem is NP-hard and they leverage special-purpose algorithms from the theory of discrete facility location to solve large problem instances efficiently. They also show that localization decisions can be taken in a distributed manner by appropriate collaboration of the clusterheads. The approach is validated in a Boston University testbed.

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