PSO for Constrained Optimization: Optimal Power Scheduling for Correlated Data Fusion in Wireless Sensor Networks

We consider the problem of optimal power scheduling for decentralized detection of a deterministic signal in a wireless sensor network with correlated observations. Each distributed sensor node independently performs amplify-and-forward (AF) processing of its observation. The fading coefficients of wireless links from distributed sensors to the fusion center (FC) are assumed to be available at transmitting nodes. When sensor observations are correlated it is difficult to derive a closed form solution for optimal power values to achieve a required fusion error performance. In this work, we develop an evolutionary computation technique based on Particle Swarm Optimization (PSO) to obtain the optimal power allocation under a required fusion error probability threshold constraint. It is shown that the optimal power allocation scheme turns off the nodes with poor channels and provides significant system power savings compared to that of uniform power allocation scheme especially when either the number of sensors in the system is large or the local observation quality is good.