A Balanced Cluster Head Selection Based On k-Medoids to Enhance Wireless Sensor Network Life Time

Priyanka Devi, Khushneet Kaur

Abstract: In this paper, a new clustering algorithm has been developed using k-medoids clustering algorithm for WSNs. The new algorithm is aimed to develop an algorithm, which perform better than the k-means and LEACH for WSNs. It is widely known that if cluster formation algorithm computes the cluster head according to the node density in a particular cluster. It is also known that the cluster head selection results are better in k-Medoids than k-Means. It has been already proved that k-Means performs better than LEACH. So our aim was to develop a clustering and cluster head selection algorithm based on k-Medoids which performs better than k-Means. In this paper, we have published the results of our new clustering and cluster head selection algorithm based on k-Medoids. This algorithm has improved the network lifetime than the k-Means algorithm by using the balanced cluster head selection based on the network density weight.

Keywords: WSN clustering, k-Medoids, cluster head selection, Network Lifetime