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When Wireless Charging Meets Fresnel Zones: Even Obstacles Can Enhance Charging Efficiency

机译:当无线充电遇到菲涅耳区域时:即使障碍物也可以提高充电效率

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Benefitting from the discovery of wireless power transfer (WPT) technology, the wireless rechargeable sensor network (WRSN) becomes a promising way for lifetime extension for wireless sensor networks. However, in practical applications, obstacles can be found almost everywhere throughout the WRSN system. Most prior arts believe that obstacles will always degrade signal strength, they omit such influences for computation simplicity, which contradicts to the instincts of signal propagation, yielding their methods unsuitable for realistic adoptions. In this paper, we explore the wireless signal propagation process and provide a theoretical charging model to enhance charging efficiency by leveraging obstacles. Through utilizing the concept of the Fresnel Zones (FZs), we re-formalize the wireless charging model and discretize charging power to determine the best charging spots as well as charging durations. We model such charging efficiency maximization with obstacles (EMO) problem as a submodular function maximization problem and propose a cost-efficient algorithm with approximation ratio (e-1)/ε (1 - ε) to solve it. Finally, test-bed experiments and simulations are conducted to verify that our schemes outperform comparison algorithms by at least 10% in charging efficiency improvement.
机译:得益于无线功率传输(WPT)技术的发现,无线可充电传感器网络(WRSN)成为延长无线传感器网络寿命的一种有前途的方式。但是,在实际应用中,整个WRSN系统中几乎到处都存在障碍。大多数现有技术认为,障碍总是会降低信号强度,为简化计算它们会忽略这种影响,这与信号传播的本能相矛盾,从而导致其方法不适合实际采用。在本文中,我们探索了无线信号传播过程,并提供了一种理论上的充电模型,以通过利用障碍物来提高充电效率。通过利用菲涅耳区(FZs)的概念,我们重新制定了无线充电模型的形式,并离散化了充电功率,以确定最佳的充电点以及充电时间。我们将带有障碍物的充电效率最大化(EMO)问题建模为子模函数最大化问题,并提出一种具有近似比率(e-1)/ε(1-ε)的具有成本效益的算法来解决该问题。最后,进行了试验台实验和仿真,以验证我们的方案在改善充电效率方面优于比较算法至少10%。

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