Comparative Analysis of SGO and PSO for Clustering in WSN

Abstract

Wireless sensor network (WSN) allows nodes to monitor the alterations in the environment and to communicate to other nodes in the network. However, WSNs have finite resources. The Wireless sensors have a limited battery life, which in turn affects the life of the entire network. Energy dissipation is the key issue. Sensors use considerable energy for communicating amongst themselves. The distance between the sensors is a major cause for this energy dissipation. Therefore, reducing the communication distance can greatly benefit the network life. To preserve energy and minimize the transmission distance, clustering is one solution. Data communication from one sensor to other at a large scale consumes more energy limited transmissions are possible through clustering. Load balancing is achieved in clustering through data aggregation and this helps to prolong the lifetime of network. This paper proposes a fitness function that can be used to form clusters with energy consideration. Particle swarm optimization (PSO) and Social group optimization (SGO) are implemented with the proposed fitness equation and their performances are studied.