Data aggregation in wireless sensor network using node clustering algorithms — A comparative study

One of the most important parameters 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 WSNs is clustering and data summarization. Several energy aware, communication aware, coverage aware, data dissemination and data aggregation/sensor fusion protocols and algorithms have been specifically designed for WSN to reduce the power consumption. 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 base station or sink. 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 study are Data relay K-means clustering algorithm, 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.