Energy Efficient Ensemble K-means and SVM for Wireless Sensor Network

Abstract

A wireless sensor network (WSN) consists of a large number of small sensors with limited energy. For many WSN applications, prolonged network lifetime is important requirements. There are different techniques have already been proposed to improve energy consumption rate such as clustering ,efficient routing , and data aggregation. In this paper, we present a novel technique using  clustering .The different clustering algorithms also differ in their objectives. Sometimes Clustering  suffers from more overlapping  and redundancy data since sensor node's position is in  a critical position does not  know in which clustering it  is belonging. One option is to assign these nodes to both clusters, which is equivalent to overlap of nodes and data redundancy occurs. This paper has proposed a new method to solve this problem and make use of the advantages of Support Vector Machine SVM to strengthen K-MEANS clustering algorithm and give us  more accurate dissection boundary for each classes .The new algorithm is called K-SVM.Numerical experiments are carried out using Matlab to  simulate sensor fields. Through comparing with classical  K-MEANS clustering scheme we confirmed  that  K-SVM   algorithm  has a better improvement in clustering accuracy in these networks.