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Joint Mobile Data Gathering and Energy Provisioning in Wireless Rechargeable Sensor Networks

机译:无线可充电传感器网络中的联合移动数据收集和能量供应

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The emerging wireless energy transfer technology enables charging sensor batteries in a wireless sensor network (WSN) and maintaining perpetual operation of the network. Recent breakthrough in this area has opened up a new dimension to the design of sensor network protocols. In the meanwhile, mobile data gathering has been considered as an efficient alternative to data relaying in WSNs. However, time variation of recharging rates in wireless rechargeable sensor networks imposes a great challenge in obtaining an optimal data gathering strategy. In this paper, we propose a framework of joint wireless energy replenishment and anchor-point based mobile data gathering (WerMDG) in WSNs by considering various sources of energy consumption and time-varying nature of energy replenishment. To that end, we first determine the anchor point selection strategy and the sequence to visit the anchor points. We then formulate the WerMDG problem into a network utility maximization problem which is constrained by flow, energy balance, link and battery capacity and the bounded sojourn time of the mobile collector. Furthermore, we present a distributed algorithm composed of cross-layer data control, scheduling and routing subalgorithms for each sensor node, and sojourn time allocation subalgorithm for the mobile collector at different anchor points. We also provide the convergence analysis of these subalgorithms. Finally, we implement the WerMDG algorithm in a distributed manner in the NS-2 simulator and give extensive numerical results to verify the convergence of the proposed algorithm and the impact of utility weight, link capacity and recharging rate on network performance.
机译:新兴的无线能量传输技术可为无线传感器网络(WSN)中的传感器电池充电并维持网络的永久运行。该领域的最新突破为传感器网络协议的设计开辟了新的领域。同时,移动数据收集已被认为是WSN中数据中继的有效替代方案。但是,无线可充电传感器网络中充电速率的时间变化对获得最佳数据收集策略提出了巨大挑战。在本文中,我们考虑了各种能源消耗和能源补充的时变性质,提出了无线传感器网络中无线能量补充和基于锚点的移动数据收集(WerMDG)的联合框架。为此,我们首先确定锚点选择策略和访问锚点的顺序。然后,我们将WerMDG问题公式化为网络效用最大化问题,该问题受流量,能量平衡,链路和电池容量以及移动收集器的有限停留时间的约束。此外,我们提出了一种分布式算法,该算法由跨层数据控制,每个传感器节点的调度和路由子算法以及移动收集器在不同锚点的驻留时间分配子算法组成。我们还提供了这些子算法的收敛性分析。最后,我们在NS-2仿真器中以分布式方式实现WerMDG算法,并给出大量数值结果,以验证所提算法的收敛性以及公用事业权重,链路容量和充电率对网络性能的影响。

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