A Multi-objective Weight based Optimal Cluster Head Selection for Lifetime Augmentation in IoT based Heterogeneous Wireless Sensor Networks

Abstract

Wireless sensor network is the saluted technology to gather information and monitor the environment, despite its limited lifetime being the major challenge. The most conferred network lifetime augmentation method in hierarchical heterogeneous sensor networks is the clustering and cluster head selection. In this paper, sensor nodes are clustered initially based on their location information and then a multi-objective weight based fitness function is used to elect the optimal cluster head among the nodes of variable energy levels. The various criterion functions considered to elect the cluster head are the residual energy, distance from the sink node, average distance of nodes in the cluster, and average energy in the cluster. The data collection is done using test beds of the setup state phase and steady-state phase. The simulation results have proven that the proposed algorithm achieves 40% increase in the network lifetime compared with pre-existing algorithms.