Multi-Swarm Particle Swarm Optimization for Energy-Effective Clustering in Wireless Sensor Networks

Wireless Sensor Networks (WSN) is composed of a large number of small nodes with limited functionality. The most important issue in this type of networks is energy constraints. In this area several researches have been done from which clustering is one of the most effective solutions. The goal of clustering is to divide network into sections each of which has a Cluster Head (CH). The task of cluster heads collection, data aggregation and transmission to the base station is undertaken. Choosing CHs in WSN in a Non-deterministic Polynomial-hard issue because optimum data collection with effective energy conservation is not capable of being resolved in polynomial time. In the current work, novel variations of Particle Swarm Optimization (PSO) are presented which are particularly formulated for excellent functioning in dynamic settings. The primary notion is the extension of single population PSO as well as charged PSO techniques through the construction of interactive multi-swarms. Updating as well as recalculating algorithms for connected dominating set is also proposed for when topologies of ad hoc wireless networks change. Exhaustive simulations reveal that the suggested method performs excellently in comparison to PSO as well as Hybrid Energy-Effective Distributed clustering protocols.