Bayesian fuzzy hypothesis test in wireless sensor networks with noise uncertainty

Reliable event detection is an essential task of wireless sensor networks (WSNs) in which there are different types of uncertainty. In this paper, we consider a decentralized detection problem for a WSN and use fuzzy hypothesis test (FHT) in the Bayesian perspective to model the noise power uncertainty. FHT employs membership functions as hypotheses for modeling and analyzing the uncertainty. Using Bayesian FHT (BFHT), a local detector scheme is proposed at each sensor node in which the threshold depends on the noise power uncertainty bound. Local decisions of sensors are sent to the fusion center (FC) and combined to make a final decision about the absence/presence of the event. The proposed algorithm is evaluated in terms of probabilities of detection and false alarm. Simulations show that the proposed BFHT detector considerably outperforms the Anderson–Darling method as well as the conventional energy detector in the presence of the noise power uncertainty.