Decision Fusion in Distributed Cooperative Wireless Sensor Networks

In this paper, the distributed decision fusion (DF) problem of cooperative wireless sensor networks is considered, where a group of sensors send their local decisions to a fusion center via a set of relays using decode-and-forward relaying. The likelihood ratio test under Neyman–Pearson sense is used to derive the optimal DF rule (DFR). Moreover, three suboptimal fusion rules are derived to enable a broad range of complexity-performance tradeoffs. The performance of the optimal and suboptimal fusion schemes is evaluated in terms of probability of detection, false alarm, and fusion decision error where analytical expressions are derived for the suboptimal schemes, while Monte Carlo simulation is used for the optimal DFR. The obtained analytical and simulation results show that the decision error performance of the network is mainly determined by the number and accuracy of the local sensors, while the relaying process could be effective in mitigating the fading channel effects. Furthermore, no significant improvement is achieved by increasing the number of relays beyond three for each sensor.