Distributed detection by multiple tests in sensor networks using range information

We consider the problem of binary distributed detection when the target position is unknown in the context of large-scale, dense sensor networks. We propose to divide the area where the target could be present into smaller ones, performing a log-likelihood ratio fusion rule in each one. We derive the Bayesian and NP fusion rules under using a model of probability of detection that makes no assumptions on the local decision rule. The performances of both tests is analyzed using large deviation bounds on the error probability and a parametric approximation to the probability of detection function. The main conclusions of the analysis of these bounds are that, for designing efficient tests in terms of energy consumption, 1) the exploration area for each test must cover the area in which the target could be present extended by a distance that is less or equal to the range of the local sensors, depending on the type (Bayes or NP) test, and, 2) the pattern for dividing the large area into smaller ones is the area inside the range of a local sensor.