An Efficient Fitness Function for Clustering of Wireless Sensor Networks

Background & Objective: A sensor network is composed of a large number of sensor nodes that are deployed to perform measurement and/or command and control in a field. Sensor nodes are battery powered devices and replacement or recharging of their batteries may not be feasible. One of the major challenges with sensory wireless networks is excessive energy consumption in nodes. Clustering is one of the methods that has been offered for resolving this issue. In this paper, we pursue evolutionary clustering and propose a new fitness function that har-nesses multiple propagation indices. Methods: In this paper we develop an efficient fitness function by first selecting the best clusters, and then selecting the best attribution of cluster to clusters. The distance between the nodes and relevant cluster heads was used for the mathematical modelling necessary. In the end we develop the fitness function equation by using normalization of the raw data. Results: Simulation results show improvement compared to previous fitness functions in clustering of the wireless sensor networks.