Statistical location detection with sensor networks

  • Authors:

  • Saikat Ray;Wei Lai;Ioannis Ch. Paschalidis

  • Affiliations:

  • Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA;Center for Information and Systems Engineering, and Department of Manufacturing Engineering, Boston University, Brookline, MA;Center for Information and Systems Engineering, and Department of Manufacturing Engineering, Boston University, Brookline, MA

  • Venue:
  • IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
  • Year:
  • 2006

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Abstract

The paper develops a systematic framework for designing a stochastic location detection system with associated performance guarantees using a wireless sensor network. To detect the location of a mobile sensor, the system relies on RF-characteristics of the signal transmitted by the mobile sensor, as it is received by stationary sensors (clusterheads). Location detection is posed as a hypothesis testing problem over a discretized space. Large deviations results enable the characterization of the probability of error leading to a placement problem that maximizes an information-theoretic distance (Chernoff distance) among all pairs of probability distributions of observations conditional on the sensor locations. The placement problem is shown to be NP-hard and is formulated as a linear integer programming problem; yet, large instances can be solved efficiently by leveraging special-purpose algorithms from the theory of discrete facility location. The resultant optimal placement is shown to provide asymptotic guarantees on the probability of error in location detection under quite general conditions by minimizing an upper bound of the error-exponent. Numerical results show that the proposed framework is computationally feasible and the resultant clusterhead placement performs near-optimal even with a small number of observation samples in a simulation environment.