An energy-efficient clustering algorithm for data gathering and aggregation in sensor networks

Energy-efficient data gathering is a common but critical operation in many applications of wireless sensor networks. Clustering is a kind of key technique used to reduce energy consumption, which can decrease the communication load and prolong the network lifetime by means of similar data aggregation in the cluster-heads. In this paper, we propose a novel clustering algorithm which better suit the periodical data gathering applications. Our approach first use genetic algorithm to partition the adjacent nodes which will sense similar target into one cluster, then elects cluster-heads with more residual energy and fewer intra-cluster communication cost. Since improving the rate of data aggregation in clusters, our approach can effectively reduce redundant data transmission and the whole energy consumed in the network. Our experimental results demonstrate that the proposed algorithms significantly outperform previous methods, in terms of system lifetime.