Research on lifetime prediction-based recharging scheme in rechargeable WSNs

Yang Yang, He Li, Xue-song Qiu, Shaoyong Guo, XiaoXiao Zeng, Kangting Zhao, Haoran Xin
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引用次数: 2

Abstract

In order to reduce the cost and energy consumption in wireless sensor network's charging process, this paper proposes a Recharging Scheme based on Lifetime Prediction (RSLP) for wireless rechargeable sensor networks. First of all, based on the historical quantity of electricity variation sequence of the sensor nodes, the lifetime prediction scheme of the sensor nodes is established; and then, considering the sensor nodes need to be recharged and the Sink nodes chosen by the mobile charger (MC) according to the charging value to establish an undirected complete diagram. A Hamilton charging circuit is established by using the Gene-Expressive cuckoo algorithm to solve the charging problem of the rechargeable sensor networks. The simulation experiments show that the proposed algorithm can improve charging efficiency and reduce the mobile energy consumption.
基于寿命预测的可充电无线传感器网络充电方案研究
为了降低无线传感器网络充电过程中的成本和能耗,提出了一种基于寿命预测(RSLP)的无线可充电传感器网络充电方案。首先,根据传感器节点的历史电量变化序列,建立传感器节点的寿命预测方案;然后,考虑需要充电的传感器节点和移动充电器(MC)根据充电值选择的Sink节点,建立无向完全图。利用基因表达布谷鸟算法建立了Hamilton充电电路,解决了可充电传感器网络的充电问题。仿真实验表明,该算法可以提高充电效率,降低移动能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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