Decentralized detection in sensor networks using range information

We consider the problem of binary distributed detection in the context of large-scale, dense sensor networks. We propose to model the probability of detection in each sensor, p_d, as a function of the distance between the sensor and the source or target to be detected. We derive the Bayesian fusion rule under that model. We also derive, using the asymptotic Gaussianity of the log-likelihood ratio, the Neyman-Pearson fusion rule. The performance of both tests is analyzed using large deviation bounds on the error probability and a parametric approximation to p_d. The main conclusions of the analysis of these bounds are that, for designing efficient tests in terms of energy consumption, (1) the sensors must be grouped in areas of the order of the range of the local detectors, and, (2) the sensor must be configured to achieve the best local discrimination between hypothesis, independently of the configuration of the network.