An Energy Efficient Clustering Approach Using Spectral Graph Theory in Wireless Sensor Networks

TLDR: The novel and efficient clustering called Clustering using Eigen Values (CEV) is proposed in this paper with the increased lifetime of the sensor nodes using the spectral graph theory.

Abstract: Grouping the nodes and form a cluster with less energy consumption by that maximizing the life time is an challenging task in WSNs. The two important steps in clustering are Cluster formation and Cluster Head (CH) selection. The novel and efficient clustering called Clustering using Eigen Values (CEV) is proposed in this paper with the increased lifetime of the sensor nodes using the spectral graph theory. This work uses the Laplacian matrix of spectral theory for clustering. The Eigen values of Laplacian Matrix and its corresponding eigenvector are used to group the nodes of WSN. CH is selected using fuzzy logic and constraints on energy and distance. This work is evaluated and compared with LEACH and HEED for performance comparison. The results obtained in this work show that the proposed work yields better performance when compared to other existing cluster based techniques based on the parameters such as network lifetime and energy consumption.