In this paper, we consider the scenario in which a mobile charger (MC) periodically travels within a sensor network to recharge the sensors wirelessly. We design joint charging and scheduling schemes to maximize the Quality of Monitoring (QoM) for stochastic events, which arrive and depart according to known probability distributions of time. Information is considered captured if it is sensed by at least one sensor. We focus on two closely related research issues, i.e., how to choose the sensors for charging and decide the charging time for each of them, and how to schedule the sensors’ activation schedules according to their received energy. We formulate our problem as the maximum QoM CHArging and SchEduling problem (CHASE). We first ignore the MC's travel time and study the resulting relaxed version of the problem, which we call CHASE-R. We show that both CHASE and CHASE-R are NP-hard. For CHASE-R, we prove that it can be formulated as a submodular function maximization problem, which allows two algorithms to achieve $1/6$1/6- and $1/(4 + \epsilon)$1/(4+ε)-approximation ratios. Then, for CHASE, we propose approximation algorithms to solve it by extending the CHASE-R results. We conduct simulations to validate our algorithm design.

中文翻译：

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CHASE：无线可充电传感器网络中随机事件捕获的充电和调度方案

在本文中，我们考虑移动充电器 (MC) 定期在传感器网络内移动以无线方式为传感器充电的场景。我们设计了联合充电和调度方案，以最大限度地提高随机事件的监控质量 (QoM)，这些事件根据已知的时间概率分布到达和离开。如果信息被至少一个传感器感测到，则认为信息被捕获。我们关注两个密切相关的研究问题，即如何选择用于充电的传感器并决定每个传感器的充电时间，以及如何根据接收到的能量来安排传感器的激活时间表。我们将我们的问题表述为最大 QoM查房环和 秒ch乙双重问题（CHASE）。我们首先忽略 MC 的旅行时间并研究由此产生的问题的宽松版本，我们称之为 CHASE-R。我们表明 CHASE 和 CHASE-R 都是 NP-hard。对于 CHASE-R，我们证明它可以表述为一个子模函数最大化问题，它允许两种算法实现$1/6$1/6- 和 $1/(4 + \epsilon)$1/(4+ε)- 近似比率。然后，对于 CHASE，我们提出了近似算法，通过扩展 CHASE-R 结果来解决它。我们进行模拟以验证我们的算法设计。