Energy-efficient Optimization of Reorganization-Enabled Wireless Sensor Networks

This paper studies the target tracking problem in wireless sensor networkswhere sensor nodes are deployed randomly. To achieve tracking accuracy constrained byenergy consumption, an energy-efficient optimization approach that enablesreorganization of wireless sensor networks is proposed. The approach includes threephases which are related to prediction, localization and recovery, respectively. A particlefilter algorithm is implemented on the sink node to forecast the future movement of thetarget in the first prediction phase. Upon the completion of this phase, the most energyefficient sensor nodes are awakened to collaboratively locate the target. Energy efficiencyis evaluated by the ratio of mutual information to energy consumption. The recoveryphase is needed to improve the robustness of the approach. It is performed when thetarget is missed because of the incorrect predicted target location. In order to recapture thetarget by awakening additional sensor nodes as few as possible, a genetic-algorithm-basedmechanism is introduced to cover the recovery area. We show that the proposed approachhas excellent tracking performance. Moreover, it can efficiently reduce energyconsumption, prolong network lifetime and reduce network overheads.