Analysis of Node Clustering Algorithms on Data Aggregation in Wireless Sensor Network

Abstract

One of the most important constraints to be studied in Wireless Sensor Networks (WSNs) is its life time. There are two typical data mining processes that support to reduce the energy consumption of WSN is clustering and data summarization. One of the primary goals of node clustering in WSN is in-network preprocessing that aims to obtain qualified information and to limit the energy consumed. A clustering algorithm is composed of three parts first electing cluster head (CH), selection of cluster membership and transferal data from members to CH.CH relays only one of the aggregated or compressed data packet to sink/ base station. In this paper a brief comparative study is made from different research proposals, which suggests different cluster head selection approaches for data aggregation. The algorithms under this study are Voronoi based K-means clustering algorithm, Voronoi Fuzzy C-means clustering algorithms and Voronoi based Genetic clustering algorithm. Significant factors for evaluating and comparing these algorithms are defined, analyzed and summarized. It has been assumed that the sensor nodes are randomly distributed and are not mobile, the coordinates of the base station (BS) and the dimensions of the sensor field are known.