Energy Efficient and Fuzzified Clustering model for Wireless Sensor Network

Abstract

Wireless sensor network is a collection of sensor nodes distributed over a target area.  The life span of wireless sensor network is mainly constrained by limited available of each sensor node’s energy. Thus. It is very straight forward that life span of WSN can increase by reducing the energy consumption without compromising its efficiency. In this context, the work carried in this paper proposes a new and energy efficient clustering model for wireless sensor network.  The design the clustering model for wireless sensor network consists of two steps. In the first step, a modified k-Means algorithm is proposed to group sensor nodes into clusters. The addition of a sensor node to a particular cluster is governed by its distance from the cluster node as well as its connectivity to at least one-member node of that cluster. Cluster head of each cluster is selected in the second step of this proposed model. A new Fuzzy inference system is proposed for selection of the cluster heads. The input to this system is the fuzzified values of three crisp variables viz. residual energy, distance from centroid and the distance from the base station. Four different levels of residual energy are considered namely, Low, Medium, Less High and High.  Other two variables as mentioned above have three different levels: Close, Medium and Far. The output crisp variable i.e. Chance has six levels: Low, Less Medium, Medium, More medium, Less High and High. The output of FIS is defuzzied to its crisp equivalent which is used for sensor node selection. The proposed model is compared with some existing method of same interest in term of the overall life span. From this comparison, it can be ensured that the proposed model is more energy efficient than its counterpart methods.