Optimal Linear Fusion Rule for Distributed Detection in Clustered Wireless Sensor Networks

In this paper we consider the distributed detection of intruders in clustered wireless sensor networks (WSNs). The WSN is modelled by a homogeneous Poisson point process (PPP). The sensor nodes (SNs) compute local decisions about the intruder's presence and send them to the cluster heads (CHs). Hence, the CHs collect the number of detecting SNs in the cluster. The fusion center (FC), on the other hand, combines the the CH's data in order to reach a global detection decision. We propose an optimal cluster-based linear fusion (OCLR), in which the CHs' data are linearly fused. Interestingly, the OCLR performance is very close to the optimal clustered fusion rule (OCR) previously proposed in literature. Furthermore, the OCLR performance approaches the optimal Chair-Varshney fusion rule as the number of SNs increases.