Optimizing rule weights to improve FRBS clustering in wireless sensor networks

Abstract

Wireless sensor networks (WSNs) are usually composed of tenths or hundredths of nodes powered by batteries that need efficient resource management to achieve the WSN goals. One of the techniques used to manage WSN resources is clustering, thus nodes are grouped into clusters around a cluster head (CH) which must be chosen carefully. In this article, it is presented a new centralized clustering algorithm based on a Type-1 fuzzy logic controller that infers the probability for each node of becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded after that point until the last node runs out of power. These three KBs have been obtained from the original KB designed by the authors after an optimization process. It is based on a Particle Swarm Optimization algorithm that maximizes the lifetime of the WSN in those three periods by adjusting each rule in those KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make it unaffordable. The results of the present optimized approach improve significantly upon those from other authors with similar methods. Finally, the paper presents an analysis on why some rule weights change more than others in order to design more suitable controllers in the future.