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A Hybrid Framework Combining Solar Energy Harvesting and Wireless Charging for Wireless Sensor Networks

机译:用于无线传感器网络的太阳能收集和无线充电的混合框架

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Recently, there have been a growing number of applications that power wireless sensor networks (WSNs) by wireless charging technology. Although previous studies indicate that wireless charging can deliver energy reliably, it still faces regulatory challenges to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies that wireless charging can provide. Fortunately, solar energy harvesting can provide high power density which is also risk-free. However, it is subject to weather dynamics. Therefore, in this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. First, we study a placement problem on how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1.61(1 + ∈)~2-approximation algorithm for the placement. Second, we establish an energy balance in the network and explore how to maintain such balance when sunlight is unavailable. Third, we consider combining wireless charging and mobile data gathering in a joint tour in such networks, and propose a polynomial-time scheduling algorithm. Our extensive simulation demonstrates that the hybrid framework can reduce battery depletion by 20% and save system cost by 25% compared to previous results.
机译:最近,通过无线充电技术能够通过无线传感器网络(WSN)提供越来越多的应用程序。尽管以前的研究表明,无线充电可以可靠地提供能量,但它仍然面临着提供高功率密度的监管挑战,而不会产生健康风险。特别地,在聚类WSA中,存在来自集群头部的高能量需求与无线充电可以提供的相对低的能量供应之间存在不匹配。幸运的是,太阳能收获可以提供高功率密度,这也是无风险的。但是,它受到天气动态的影响。因此,在本文中,我们提出了一种混合框架,将两种技术 - 簇头配备有太阳能电池板,以清除太阳能,其余的节点由无线充电供电。首先,我们研究了如何部署太阳能集群头的放置问题,可以最大限度地减少总成本,并提出分布式1.61(1次)〜2近似算法进行放置。其次,我们在网络中建立了能量平衡,并在阳光下不可用时探索如何保持这种平衡。第三,我们考虑在这种网络中的联合巡视中结合无线充电和移动数据,并提出多项式时间调度算法。我们的广泛模拟表明,与先前的结果相比,混合框架可以将电池耗竭降低20%,并将系统成本降低25%。

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