POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors
提出一种分布式监督控制算法POSE，让传感器节点根据目标预测位置动态切换高/低功耗设备，在减少能耗的同时通过融合初始化提升跟踪精度，经蒙特卡洛仿真验证有效。
This paper presents a distributed supervisory control algorithm that enables opportunistic sensing for energy-efficient target tracking in a sensor network. The algorithm called Prediction-based Opportunistic Sensing (POSE), is a distributed node-level energy management approach for minimizing energy usage. Distributed sensor nodes in the POSE network self-adapt to target trajectories by enabling high power consuming devices when they predict that a target is arriving in their coverage area, while enabling low power consuming devices when the target is absent. Each node has a Probabilistic Finite State Automaton which acts as a supervisor to dynamically control its various sensing and communication devices based on target's predicted position. The POSE algorithm is validated by extensive Monte Carlo simulations and compared with random scheduling schemes. The results show that the POSE algorithm provides significant energy savings while also improving track estimation via fusion-driven state initialization.