Standard deviation based weighted clustering algorithm for wireless sensor networks

Clustering in wireless sensor networks (WSN) is a proven technique to avoid redundant transmissions to the sink for better utilization of scarce network resources such as energy. Most of the clustering algorithms proposed in literature involves high number of message exchanges which results in unnecessary energy consumption. In this paper, we propose a standard deviation based weighted cluster head selection algorithm to avoid such message exchanges. The proposed mechanism uses distance and connectivity as two key parameters for optimal cluster head selection. Simulation results show that the proposed algorithm results in low packet drop, delay and control overhead.