最优$k$ -覆盖计费问题

摘要：

无线可充电传感器网络是由带有可充电电池的传感器节点和用于充电的移动充电器组成的，它逐渐成为解决无线传感器网络（WSN）能量受限瓶颈的一种有效方法。在本文中，我们专注于移动充电器调度和路径优化的情况下，其中的$k$覆盖能力的网络系统需要保持。我们制定了最佳的$k$覆盖充电问题的移动充电器充电的传感器节点在其估计的充电期限内的约束下，保持$k$覆盖能力的网络系统，以最小化每次旅行的能源消耗的目标。我们的困难的问题，即使找到一个可行的路径的平凡的情况下的问题是一个NP完全的。我们模型的问题，并应用动态规划设计一个算法，找到一个精确的解决方案，最佳的$k$覆盖收费问题。然而，计算复杂性对于大规模网络仍然是禁止的。然后，我们介绍了深度Q学习，一种强化学习算法来解决这个问题。

英文摘要：

Wireless rechargeable sensor networks, consisting of sensor nodes with rechargeable batteries and mobile chargers to replenish their batteries, have gradually become a promising solution to the bottleneck of energy limitation that hinders the wide deployment of wireless sensor networks (WSN). In this paper, we focus on the mobile charger scheduling and path optimization scenario in which the $k$-coverage ability of a network system needs to be maintained. We formulate the optimal $k$-coverage charging problem of finding a feasible path for a mobile charger to charge a set of sensor nodes within their estimated charging deadlines under the constraint of maintaining the $k$-coverage ability of the network system, with an objective of minimizing the energy consumption on traveling per tour. We show the hardness of the problem that even finding a feasible path for the trivial case of the problem is an NP-complete one. We model the problem and apply dynamic programming to design an algorithm that finds an exact solution to the optimal $k$-coverage charging problem. However, the computational complexity is still prohibitive for large size networks. We then introduce Deep Q-learning, a reinforcement learning algorithm to tackle the problem.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。