Multi-Hop Wireless Sensor Network Clustering Optimization Using Genetic Algorithm(SMO)

Abstract

A wireless sensor network is a collection of sensor nodes that gather data from the physical environment and transmit it wirelessly to a base station. These nodes have limited resources in terms of energy and bandwidth. To route traffic from source to destination, hierarchical-based routing protocols are used, which divide the network into clusters and create a hierarchy of nodes. In previous research, fuzzy decision rules have been used to select cluster heads based on parameters such as residual energy. However, in this study, a genetic-based approach was proposed, which uses selection, crossover, and mutation to select the best sensor node as the cluster head. Performance parameters such as dead node count, alive node count, and residual energy were used to evaluate the proposed approach. The results show that the genetic-based approach outperforms the fuzzy-based approach, leading to an improved network lifetime.