Improved node localization using K-means clustering for Wireless Sensor Networks

Abstract A power-efficient K-means clustering algorithm for Wireless Sensor Networks (WSN) is proposed. This algorithm aims to manage the consumption of energy by WS nodes and enhance the running time for WSN given space constraints. WS node cluster formation is structured as a sample space partition in k-means for the reason that the radio channel is unstable and the distribution of the nodes is coarse. After measuring the overall network energy consumption, the optimal Cluster Heads (CH’s) are evaluated on the basis of network size. The length of space from CH to node is evaluated and the membership weight is considered for the objective function. We propose an approach for making numerous node clusters using an improved K-means clustering algorithm called Optimal K-means (OK-means). A single hop communication mode is employed for intra-cluster communication whereas a multi-hop communication mode is used by the inter-cluster communication. The performance is evaluated using Ns-2 simulator. The outputs of these simulations show that the proposed algorithm achieves uniform distribution in spatial domain of CH. Which effectively balance the energy consumption. Further, extensive simulations have been carried out by varying node densities to demonstrate the full potential of OK-means.