Abstract: Energy constraint is a critical problem for practical applications in wireless sensor networks. Focused on the energy efficiency problem of wireless sensor networks, a dynamic power optimization method is proposed with target prediction. Particle filter is introduced to predict target state, which is adopted to awaken wireless sensor nodes so that their sleep time is prolonged. With the distributed computing capability of nodes, distributed genetic algorithm and simulated anneal is proposed to optimize the energy consumption of measurement. Considering the application of target tracking, target position prediction, node sleep scheduling and optimal sensing node selection are implemented. Moreover, a routing scheme of for-warding nodes is presented to achieve extra energy conserva-tion. Experimental results of target tracking verified that en-ergy-efficiency is enhanced by dynamic power optimization with target prediction in wireless sensor networks.

Key words: Dynamic power management, Energy efficiency, Particle filter, Wireless sensor networks