Closeness Centrality Based Cluster Head Selection Algorithm for Large Scale WSNs

Abstract

Low-energy adaptive clustering hierarchy (LEACH) is a adaptive clustering routing protocol, which is proposed to efficiently manage the energy consumption in Wireless Sensor Networks (WSNs). In this protocol, sensor nodes are organized into clusters and elects the cluster heads randomly. Sensor nodes in each cluster transmit the data directly to the cluster heads. Cluster heads gather the data and transmits to base station. Here, random selection of cluster heads helps to distribute the energy dissipation evenly among all sensor nodes. However, this mechanism consume relatively more energy consumption in large scale WSNs, as the distance between newly elected cluster head and sensor nodes may not be optimal. Intuitively, LEACH protocol with deterministic selection of cluster head based on minimum distance between all sensor nodes in the cluster consumes less energy over random selection. Although, this method increases the life time of the WSN, it is computationally inefficient for large-scale WSNs. In our work, we select the cluster head based on closeness centrality measure. We observe a significant reduction of energy consumption over LEACH protocol with less computational complexity. We also prove that deterministic selection of cluster head based on closeness centrality measure improves the lifetime of WSN significantly over random selection.