Hybrid Cluster Head Selection Approach for Node Lifetime Enhancement in Wireless Sensor Networks

Node lifetime is an important factor in wireless sensor networks as the entire lifetime of the network depends on the individual nodes. Researchers pay more attention towards enhancement of node lifetime through various deployment models. Rather than concentrating over node deployment, efficient clustering, data aggregation in wireless sensor networks enhances the node and network lifetime, minimize the energy utilization, reduces network congestion and identifies an optimal route for better load balancing. Clustering approaches considers the parameters like residual energy of node, communication range, distance between node and sink. Specifically, cluster head selection and replacement is a crucial part in clustering which directly relates to energy management of network. Considering these facts, an energy efficient clustering approach to enhance node lifetime through hybrid adaptive neuro fuzzy inference system (ANFIS) is proposed in this research work. Conventional models are compared with proposed hybrid approach to demonstrate the superior performance.