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CHASE: Charging and Scheduling Scheme for Stochastic Event Capture in Wireless Rechargeable Sensor Networks

机译:案例:无线充电传感器网络中随机事件捕获的计费和调度方案

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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 MCs 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)$1/(4+)-approximation ratios. Then, for CHASE, we propose approximation algorithms to solve it by extending the CHASE-R results. We conduct simulations to validate our algorithm design.
机译:在本文中,我们考虑了以下情形:移动充电器(MC)定期在传感器网络内移动,以对传感器进行无线充电。我们设计联合计费和计划方案,以最大程度地提高随机事件的监视质量(QoM),该事件根据已知的时间概率分布到达和离开。如果至少一个传感器检测到信息,则认为该信息已捕获。我们专注于两个密切相关的研究问题,即如何选择要充电的传感器并确定每个传感器的充电时间,以及如何根据传感器接收到的能量来计划传感器的激活时间表。我们将我们的问题公式化为最大QoM变更和调度问题(CHASE)。我们首先忽略MC的旅行时间,然后研究所产生的问题的轻松版本,我们称之为CHASE-R。我们显示CHASE和CHASE-R都是NP-hard。对于CHASE-R,我们证明可以将其表达为子模函数最大化问题,这使两种算法可以实现$ 1/6 $ 1 / 6-和$ 1 /(4 + epsilon)$ 1 /(4+)逼近率。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。我们进行仿真以验证我们的算法设计。

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