Sensor scheduling for energy-efficient collaborative target tracking in wireless sensor networks (WSNs) is an important problem to deal with the limited network resources. With the recent development and emerging applications of energy acquisition technologies, it has become possible to overcome the bottleneck of battery energy in WSNs using the energy harvesting devices, where theoretically the lifetime of the network could be extended to the infinite. However, the energy harvesting WSN also poses new challenges for sensor scheduling algorithm over the infinite horizon under the limited sensor energy harvesting capabilities. In this article, a novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking in energy harvesting WSNs to schedule sensors over an infinite horizon, according to the ADP mechanism. The “action” module of MSPADP is designed to obtain the sensor scheduling for multiple steps starting from the current step, and implemented by the minimal-cost first search (MCFS) decision tree scheme, and the “critic network” module of MSPADP is iteratively performed to optimize the performance for the remaining infinite steps using neural network. Extended Kalman filter (EKF) is adopted to predict and estimate the target state. The performance index is defined by the tracking accuracy derived from EKF and the energy consumption predicted by the candidate sensor schedule. Theoretical analysis shows the optimality of MSPADP, and simulation results demonstrate its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) sensor scheduling approaches. Note to Practitioners-Collaborative target tracking is a typical problem in wireless sensor networks (WSNs) where the sensors need to be scheduled to address the constraints of the limited network resources, such as sensor energy usually supplied by the battery. In the recent years, energy harvesting device has been developed and applied to WSNs to overcome the energy restriction. As the energy harvesting capabilities of the sensors are limited, sensor scheduling remains as a challenging problem and is studied in this article. A novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking, by scheduling sensors for the current time step based on the predictions of the subsequent steps over an infinite horizon. It runs iteratively in two modules: obtaining the previous optimal multistep sensor scheduling and updating the remaining infinite-step performance. Simulation results show its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) approaches, and lay a good foundation for the practical applications.

中文翻译：

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基于多步预测的自适应动态规划传感器调度方法，用于能量收集无线传感器网络中的协作目标跟踪


无线传感器网络（WSN）中用于节能协作目标跟踪的传感器调度是处理有限网络资源的一个重要问题。随着能量采集技术的最新发展和新兴应用，使用能量采集设备克服无线传感器网络中电池能量的瓶颈已经成为可能，理论上网络的寿命可以无限延长。然而，能量收集无线传感器网络也对传感器能量收集能力有限的无限视野下的传感器调度算法提出了新的挑战。在本文中，提出了一种新颖的基于多步预测的自适应动态规划（MSPADP）方法，用于能量收集 WSN 中的协作目标跟踪，根据 ADP 机制在无限范围内调度传感器。 MSPADP的“动作”模块旨在获得从当前步骤开始的多个步骤的传感器调度，并通过最小成本优先搜索（MCFS）决策树方案来实现，MSPADP的“批评网络”模块是迭代的使用神经网络来优化剩余无限步骤的性能。采用扩展卡尔曼滤波器（EKF）来预测和估计目标状态。性能指数由 EKF 得出的跟踪精度和候选传感器调度预测的能耗定义。理论分析表明了MSPADP的最优性，仿真结果表明其与基于单步预测的ADP（SSPADP）、基于多步预测的动态规划（MSPDP）和基于多步预测的剪枝（MSPP）传感器调度相比具有优越的跟踪性能接近。 从业者须知——协作目标跟踪是无线传感器网络（WSN）中的一个典型问题，其中需要对传感器进行调度以解决有限网络资源的限制，例如通常由电池提供的传感器能​​量。近年来，能量收集装置被开发出来并应用于无线传感器网络以克服能量限制。由于传感器的能量收集能力有限，传感器调度仍然是一个具有挑战性的问题，本文对此进行了研究。提出了一种新颖的基于多步预测的自适应动态编程（MSPADP）方法，用于协作目标跟踪，通过基于无限范围内后续步骤的预测来调度当前时间步的传感器。它以两个模块迭代运行：获取先前最优的多步传感器调度并更新剩余的无限步性能。仿真结果表明，与基于单步预测的ADP（SSPADP）、基于多步预测的动态规划（MSPDP）和基于多步预测的剪枝（MSPP）方法相比，其具有优越的跟踪性能，为实际应用奠定了良好的基础。