Energy-efficient Optimization of Reorganization-Enabled Wireless Sensor Networks

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