Particle swarm optimization based clustering in wireless sensor networks: The effectiveness of distance altering

Economic usage of energy is a critical issue in wireless sensor network. Network clustering is an efficient technique for minimizing node energy consumption and maximizing network lifetime. One of the major issues of a clustering protocol is selecting an optimal group of sensor nodes as the cluster heads to divide the network. But optimum clustering is an NP-Hard problem and solving it involves searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of such issue. In this paper, we explore an evolutionary algorithm to optimize the energy consumption, which is particle swarm optimization to find the optimal clusters based on residual energy and transmission distance. The simulation results demonstrate that our protocol considerably increases the network's lifespan, compared with existing clustering protocols.