An Uneven Clustering Algorithm Based on Fuzzy Theory for Wireless Sensor Networks

Abstract

In wireless sensor networks, clustering algorithm prolongs network lifetime significantly. The network is often organized into clusters of equal size, but such even clustering method results in unequal loads on the cluster heads(CHs). To balance the energy consumption of the network nodes, an uneven Clustering Algorithm based on Fuzzy Theory(FTCA) is proposed. With the consideration of both the node's location and residual energy during CHs election, the algorithm optimizes the clustering probability. Triangle module operator in fuzzy theory is used to integrate the degree of location-based membership function and that of the residual energy. And the probability for a node to be a CH is decided by fusion result. Therefore, the network is divided into uneven clusters. Simulation results show that FTCA effectively balances and reduces the energy consumption of the network, and obviously prolongs the network lifetime.