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In the paper, we consider a network of energy con-strained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station (via clusterhead) for further processing. The key problem focuses on how to reduce the power consumption of wireless microsensor networks. The core includes the energy effi-ciency of clusterheads and that of cluster members. We first ex-tend low-energy adaptive clustering hierarchy (LEACH)’s sto-chastic clusterhead selection algorithm by a factor with dis-tance-based deterministic component (LEACH-D) to reduce en-ergy consumption for energy efficiency of clusterhead. And the cost function is proposed so that it balances the energy consump-tion of nodes for energy efficiency of cluster member. Simulation results show that our modified scheme can extend the network life around up to 40% before first node dies. Through both theoretical analysis and numerical results, it is shown that the proposed algo-rithm achieves better performance than the existing representative methods.
In the paper, we consider a network of energy con-strained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station (via clusterhead) for further processing. how to reduce the power consumption of wireless microsensor networks. The core includes the energy effi-ciency of clusterheads and that of cluster members. We first ex-tend low-energy adaptive clustering hierarchy (LEACH) ’s sto-chastic clusterhead selection algorithm by a factor with dis-tance-based deterministic component (LEACH-D) to reduce en-ergy consumption for energy efficiency of clusterhead. And the cost function is proposed so that it balances the energy consump-tion of nodes for energy efficiency of cluster member . Simulation results show that our modified scheme can extend the network life around up to 40% before first node dies. Through both theoretical analysis and numerical results, it is shown that the p roposed algo-rithm achieves better performance than the existing representative methods.