Life time enhancement of wireless Sensor Network using fuzzy c-means clustering algorithm

The major issues in wireless Sensor Networks (WSNs) are efficient uses of limited resources and appropriate routing of network paths under severely constrained energy scenarios. To overcome these issues; k-means and fuzzy c-means algorithms are investigated to form clusters and for subsequent selection of cluster heads. For all these clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) and estimation of Euclidean distances. Depending upon the Euclidean distance measure between the sink node and center of gravity of clusters; these clusters are classified into five types. Further, RES estimations are presented for cluster heads as well simple sensor network nodes.