Optimal Charging in Wireless Rechargeable Sensor Networks
Optimal Charging in Wireless Rechargeable Sensor Networks
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DOI：
10.1109/tvt.2015.2391119
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发表时间：
2016-01-01
影响因子：
6.8
通讯作者：
He, Tian
中科院分区：
文献类型：
--
作者：
Fu, Lingkun;Cheng, Peng;He, Tian
近年来，无线传感器网络出现了一些新的有前途的技术，这些技术促使人们重新审视一些关键问题。通过将传感和计算能力集成到传统的射频识别（RFID）标签，无线识别和传感平台（WISP）是一个开源平台，作为无线可充电传感器网络的先驱实验平台。与传统标签不同，基于RFID的无线可充电传感器节点需要将其板载能量存储充电到阈值以上，以为其传感，计算和通信组件供电。因此，这种充电延迟对部署无线可充电传感器网络提出了独特的设计挑战。在本文中，我们解决这个问题，规划的最佳运动策略的移动的RFID阅读器，使时间在网络中的所有节点的能量阈值以上的费用最小化。首先，我们提出了一个最佳的解决方案，使用线性规划（LP）方法。为了进一步降低计算复杂度，我们引入了一个启发式的解决方案，通过离散的充电功率的2-D空间上的可证明的近似比（1 + θ）/（1 -θ）。通过广泛的评估，我们证明了我们的设计优于基于集合覆盖的设计，平均为24.7%，而计算复杂度为O（（N/N）（2））。最后，我们考虑了系统实现中的两个实际问题，并提供了参数设置的指导方针。
Recent years have witnessed several new promising technologies to power wireless sensor networks, which motivate some key topics to be revisited. By integrating sensing and computation capabilities to the traditional radio-frequency identification (RFID) tags, the Wireless Identification and Sensing Platform (WISP) is an open-source platform acting as a pioneering experimental platform of wireless rechargeable sensor networks. Different from traditional tags, an RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold to power its sensing, computation, and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the mobile RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming (LP) method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + theta)/(1 - epsilon) by discretizing the charging power on a 2-D space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7%, whereas the computational complexity is O((N/epsilon)(2)). Finally, we consider two practical issues in system implementation and provide guidelines for parameter setting.