Deployment optimization of sensornet-based stochastic location-detection systems

We propose a systematic framework for designing a stochastic indoor location detection system with associated performance guarantees using a hierarchical wireless sensor network. To detect the location of a mobile sensor, we rely on RF-characteristics of the signal transmitted by the mobile sensor, as it is received by the clusterheads. The problem of location detection is posed as a hypothesis testing problem over a discretized space. We leverage large deviations and decision theory results to characterize the probability of error and use this characterization to optimally place clusterheads. The placement problem is NP-hard and we formulate it as a linear integer programming problem. We leverage special-purpose algorithms from the theory of discrete facility location to solve large problem instances efficiently. For the resultant placement we provide asymptotic guarantees on the probability of error in location detection under quite general conditions. Numerical and simulation results show that our proposed framework is computationally feasible and the resultant clusterhead placement performs near-optimum even with a small number of observation samples.