Fuzzy based Adaptive Cluster Head Selection for Wireless Sensor Networks
Priyank Gour, Neeraj Kumar Jain, Sanjeev Kumar Gupta · 2019
Clustering in wireless sensor networks is an efficient way to route data to the base station. It also conserves energy and enhances the lifetime of networks. However, many clustering techniques randomly select sensor nodes as a cluster head without considering available resources at present. Hence, many nodes exhaust their energy in an early stage of sensing and form network holes which is not suitable for reliable networks. In this paper, a fuzzy based adaptive cluster head selection (FACHS) mechanism is proposed. In this, each node considered various computational resources at the present for the cluster head selection process. These resources are fuzzified and used to formulate a threshold for cluster head selection. Simulation results show that the FACHS performs better than the existing clustering techniques LEACH, T2FLCA, and CHEETAH.