PFuzzyACO: Fuzzy-based Optimization Approach for Energy-aware Cluster Head Selection in WSN

Abstract

Cluster head selection is one of the prime challenges in the routing of the Wireless Sensor Network (WSN). Literature works have introduced various techniques for finding the optimal cluster head for establishing the communication path. The uncertainty issues prevailing in the WSN makes way for the selection of the cluster head through the optimization algorithms. In this paper, the Fuzzy based optimization approach has been introduced for the optimal selection of the cluster head. This paper proposes the Penguin Fuzzy based Ant colony optimization (PFuzzyACO) algorithm for the cluster head selection in the WSN. The proposed PFuzzyACO algorithm utilizes a multi-objective fitness function based on the parameters, such as energy, distance, delay, traffic density, and link lifetime. The results prove that the proposed PFuzzyACO algorithm behaves better than the comparative models for the system of 50 nodes with the values of 10, 0.08783, and 0.67651 for the number of alive nodes, network energy, and the throughput, respectively. The proposed PFuzzyACO model has better values of 26, 0.10857, and 0.68116 for the nodes, network energy, and the throughput respectively for WSN with 100 nodes.