Optimizing location detection services in wireless sensor networks

We propose a systematic framework for placing a given number of clusterheads in a hierarchical wireless sensor network to facilitate location detection service. The problem of location detection is posed as a hypothesis testing problem over a discretized space. Then, the clusterheads are placed in locations that maximize the worst case Chernoff distance between the conditional densities over all location pairs. Linear integer programming is used to determine the optimal placement. The resultant placement provides an asymptotic guarantee on the probability of error in location detection under quite general conditions. We obtain numerical results on the scalability of our proposed mathematical programming, and quantify the performance of the location detection system with the resultant clusterhead placement by simulation. 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.