Optimal degree centrality based algorithm for cluster head selection in wireless sensor networks

Abstract

Clustering is one of the popular methods in wireless sensor networks for achieving energy efficiency, scalability and efficient routing. Residual energy and topological features that are related to a node with respect of its structural position in the network are used for electing cluster heads. However optimal numbers of nodes that may belong to a cluster are not taken into consideration while selecting cluster heads. The proposed paper aims to define a new centrality metric "cluster optimal degree centrality". Our proposed centrality metric addresses the optimal numbers of member nodes as well as energy efficiency of a cluster. Finally based upon the defined centrality metric, a Fuzzy Inference System based cluster head selection method has been proposed. The experimental results have demonstrated that the method can effectively prolong the network lifetime and enhance cluster head selection and results in high throughput as compared to LEACH, CHEF and LEACH-ERE.