Decision Fusion in Mixing Two-Hop Cluster Based Wireless Sensor Networks

In this paper, we derive optimal likelihood based fusion rule and sub-optimal rule in mixing two-hop cluster based wireless sensor networks. The likelihood ratio rule has been shown the most optimal. However, it acquires channel knowledge and node performance which is not easily available for resource constrained sensor networks. The sub-optimal fusion rules need less computation and information. Simulation shows that sub-optimal fusion rules approach the optimal rule at different SNR.