Decentralized detection in dense sensor networks with censored transmissions

In this work, we consider the problem of detection in a sensor network with censored transmission. The motivation for this is the improvement of the energy efficiency by means of allowing just positive detection transmission. Both Bayes and NP (Neyman-Pearson) tests are developed and the performance of the NP test is studied using large deviation bounds on the error probability. We also show that these bounds using the KL divergence between the probability density functions of the observations under each hypothesis might be used as a criteria for determining the optimal exploration area or even the optimal strategy for energy efficiency. The "Spanish hat" model for the probability of detection is used to exemplify the performance of the proposed bound.