An Empirical Study of Soft Computing Approaches in Wireless Sensor Networks

Abstract

The optimal CH selection for finding the shortest path among the CHs is improved by developing the hybrid K-means with Particle Swarm Optimization (PSO) based hybrid Ad-hoc On-demand Distance Vector (AODV) channelling algorithms. The alive nodes, total packet sending time, throughput, and NL are increased using this hybrid technique, whereas dead nodes and EC are minimized in the network. The proposed algorithm utilizes a rotational method of utilization of cluster head (CH) to ensure that all member nodes are utilized uniformly based on the incoming traffic. The proposed algorithm has been implemented, experimented with, and compared in performance with LEACH, DLBA and GLBA algorithms. The proposed hybrid approach outperforms the existing techniques regarding average energy consumption and load distribution.