Energy Efficient Target Tracking Using Sleep Scheduling Mechanism for Wireless Sensor Network

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In the development of various large-scale Wireless Sensor Network systems, a particular challenging problem is how to dynamically organize the sensors network and route sensed information from the field sensors to a target system. A target tracking system is often required to ensure continuous monitoring, there always exist nodes that can detect the target along its trajectory (e.g., with low detection delay or high coverage level). Therefore, the most stringent criterion of target tracking is to track with zero detection delay or 100 percent coverage. Since nodes often run on batteries that are generally difficult to be recharged once deployed, energy efficiency is a critical feature of WSNs for the purpose of extending the network lifetime.The prime motivation of the work proposed to develop a energy efficient target tracking schemes. Toward this objective, the project uses a new energy-efficient dynamic optimization-based sleep scheduling and target prediction technique for large scale sensor networks. A probability-based prediction and optimization-based sleep scheduling protocol (PPOSS) is proposed to improve energy efficiency. A cluster-based scheme is exploited for optimization-based sleep scheduling. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster a non myopic rule is used based on not only the target state prediction but also its future tendency. Finally, the effectiveness of the proposed approach is evaluated and compared with the state-of-the-art protocols in terms of tracking accuracy, inter-node communication, and computation complexity.