On optimal scheduling of multiple mobile chargers in wireless sensor networks
On optimal scheduling of multiple mobile chargers in wireless sensor networks
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DOI：
10.1145/2633675.2633676
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
R. Beigel;Jie Wu;Huanyang Zheng
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文献类型：
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作者：
R. Beigel;Jie Wu;Huanyang Zheng
多年来，传感器节点有限的电池容量已成为无线传感器网络（WSN）应用的最大障碍。基于无线能量传输的可充电电池的最新突破为移动车辆在无线传感器网络中的应用提供了广阔的前景。这些移动车辆充当移动充电器，以有效的方式将能量无线传输到静态传感器。在本文中，我们研究了一维线和环上分布的传感器节点的移动充电器覆盖问题。每个传感器都需要以给定的频率充电。移动充电器移动到传感器所在位置后即可为传感器充电。我们假设移动充电器具有无限的充电能力，以给定限制的速度移动，并且充电时间可以忽略不计。然后提出一个关于传感器时空覆盖范围的优化问题，以使它们都不会耗尽能量：（1）最少需要多少个移动充电器？ （2）给定最小移动充电桩数量，这些移动充电桩在轨迹规划上应该如何调度？给定具有相同充电频率的同类传感器，我们提供了一个具有线性复杂度的最佳解决方案，以找到最小充电次数以及实际时间表。然后，我们研究了异构传感器的扩展，并提供了一种贪婪方法，该方法与直线和环的最佳解决方案的比率恒定为 2。进行了广泛的模拟以验证所提出方案的竞争性能。
The limited battery capacities of sensor nodes have become the biggest impediment to the applications of wireless sensor networks (WSNs) over the years. Recent breakthroughs in wireless energy transfer-based rechargeable batteries provide a promising application of mobile vehicles in WSNs. These mobile vehicles act as mobile chargers to transfer energy wirelessly to static sensors in an efficient way. In this paper, we study the mobile charger coverage problem of sensor nodes distributed on a 1-dimensional line and ring. Each sensor needs to be recharged at a given frequency. A mobile charger can charge a sensor after it moves to the location of the sensor. We assume that the mobile charger has an unlimited charging capability, moves at a speed subject to a given limit, and that the charging time is negligible. An optimization problem is then presented on a time-space coverage of sensors so that none of them will run out of energy: (1) What is the minimum number of mobile chargers needed? (2) Given the minimum number of mobile chargers, how should these mobile chargers be scheduled in terms of trajectory planning? Given homogeneous sensors with the same recharging frequency, we provide an optimal solution with a linear complexity in finding the minimum number of charges, as well as the actual schedule. We then examine an extension to heterogeneous sensors and provide a greedy approach that has a constant ratio of 2 to the optimal solutions for a line and ring. Extensive simulations are conducted to verify the competitive performance of the proposed scheme.