Optimized sugeno fuzzy clustering algorithm for wireless sensor networks

Abstract

Clustering is the most common approach to achieve energy efficiency in wireless sensor networks. The existing clustering techniques exhibit some drawbacks which limit their usage for practical networks. First, cluster heads are typically selected among all sensor nodes within the network, and consequently, unbalanced clusters may be generated. Second, the controllable parameters are defined manually. Third, the protocol is not adjusted and tuned based on application specifications. In this paper, we propose an adaptive fuzzy clustering protocol (named LEACH-SF), in order to overcome the mentioned drawbacks. In LEACH-SF, fuzzy c-means algorithm is used to cluster all sensor nodes into balanced clusters, and then appropriate cluster heads are selected via Sugeno fuzzy inference system. The fuzzy inputs of the Sugeno fuzzy inference system include the residual energy, the distance from sink, and the distance from cluster centroid. Unlike the existing fuzzy-based routing protocols in which the fuzzy rule base table is defined manually, we utilize artificial bee colony algorithm to adjust the fuzzy rules of LEACH-SF. The fitness function of the algorithm is defined to prolong the network lifetime, based on the application specifications. In other words, LEACH-SF not only prolongs the lifetime, but also is applicable to any kind of application. Simulations over 10 heterogeneous wireless sensor networks show that LEACH-SF outperforms the existing cluster-based routing protocols.