Hybrid GA-Means for energy efficient clustering in wireless sensors network

Clustering in wireless sensors network is a significant aspect that decides the lifetime of the system. Even many of the well-known clustering methods have been designed keeping in mind the energy consumption and network scalability. The aim of clustering has always been on finding clusters set, reducing overall power consumption in the network, and the lifetime enhancement of the network. The increase in the lifetime comes as a result of proper load balancing among the sensor nodes. In this paper, we propose a novel clustering algorithm, GA-MEANS, to increase the network lifetime of the network and to ensure uniform distribution of cluster heads across the sensing area. The proposed algorithm is a cross of genetic algorithm and k-means clustering algorithm. The results have been compared with other algorithms and indicate the better performance of our algorithm as compared to other algorithms.