Swarm intelligence based optimization and control of decentralized serial sensor networks

Abstract

In this paper threshold design and hierarchy management of serial sensor networks employed for distributed detection is accomplished using a hybrid of ant colony optimization and particle swarm optimization. The particle swarm optimization determines the optimal thresholds, decision rules for the sensors. The ant colony optimization algorithm determines the hierarchy of sensor decision communication, affecting the accuracy. The problem of hierarchy management is known as ldquowho reports to whom?rdquo problem in sensor networks. The new algorithm is tested on a suite of 10 heterogeneous sensors. Probabilistic measures including probability of error and Bayesian risk are adopted to evaluate the performance of the sensor network. The new sensor management methodology is compared to (a) static hierarchy network, (b) a network with the best sensor at the top of the hierarchy and (c) incrementally best hierarchy. Results show 40% performance improvements in terms of Bayesian risk value.