TSCA: A Temporal-Spatial Real-Time Charging Scheduling Algorithm for On-Demand Architecture in Wireless Rechargeable Sensor Networks
TSCA: A Temporal-Spatial Real-Time Charging Scheduling Algorithm for On-Demand Architecture in Wireless Rechargeable Sensor Networks
复制标题
TSCA：无线可充电传感器网络按需架构的时空实时充电调度算法
DOI：
10.1109/tmc.2017.2703094
复制
发表时间：
2018-01-01
影响因子：
7.9
通讯作者：
Obaidat, Mohammad S.
中科院分区：
文献类型：
--
作者：
Lin, Chi;Zhou, Jingzhe;Obaidat, Mohammad S.
无线可充电传感器网络（WRSNs）中的协同充电问题是一个研究热点。借助无线能量传输技术，电能可以从无线充电车（wcv）传输到传感器，为延长网络寿命提供了新的范例。现有的协同充电技术通常采用周期性和确定性的方法，但忽略了拓扑变化和节点故障等非确定性因素的影响，不适合大规模的WRSNs。本文针对按需充电架构，提出了一种时空充电调度算法TSCA。我们的目标是最小化死节点的数量，同时最大化能源效率，以延长网络的生命周期。首先，在收集充电请求后，WCV将计算出可行的移动解决方案。然后引入基本的路径规划算法来调整充电顺序以提高效率。此外，优化是在全局级别上进行的。在此基础上，提出了一种节点删除算法，剔除低效收费节点。最后，执行节点插入算法，避免被遗弃节点的死亡。大量的仿真结果表明，与目前最先进的充电调度算法相比，我们的方案在充电吞吐量、充电效率和其他性能指标上都取得了令人满意的性能。
The collaborative charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles (WCVs) to sensors, providing a new paradigm to prolong network lifetime. Existing techniques on collaborative charging usually take the periodical and deterministic approach, but neglect influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for large-scale WRSNs. In this paper, we develop a temporal-spatial charging scheduling algorithm, namely TSCA, for the on-demand charging architecture. We aim to minimize the number of dead nodes while maximizing energy efficiency to prolong network lifetime. First, after gathering charging requests, a WCV will compute a feasible movement solution. A basic path planning algorithm is then introduced to adjust the charging order for better efficiency. Furthermore, optimizations are made in a global level. Then, a node deletion algorithm is developed to remove low efficient charging nodes. Lastly, a node insertion algorithm is executed to avoid the death of abandoned nodes. Extensive simulations show that, compared with state-of-the-art charging scheduling algorithms, our scheme can achieve promising performance in charging throughput, charging efficiency, and other performance metrics.