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A prediction model of death probability for guiding wireless recharging in sensor networks

机译:传感器网络无线充电引导死亡概率预测模型

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摘要

Wireless power transmission (WPT) technology is usually used to maintain the continuous operation of sensor nodes. However, when large amounts of data need to be processed, the node may enter an abnormal death state because it cannot be charged in time. Therefore, a prediction of node death probability is crucial to guide the charging path planning for charging vehicles. In this paper, we build an analysis model based on a Markov fluid queue (MFQ) model with the aim of creating harvest-store-use (HSU) and harvest-then-use (HTU) models of the node. Specifically, the proposed models involve a Markov process, a queuing model, and a successive fluid process. The result shows that the abnormal death probability calculated by the model is approximately 0.1 different from the probability of death obtained by simulation. Meanwhile, by comparing the two modes of energy usage, we find that HTU is better than HSU.
机译:无线电力传输(WPT)技术通常用于维持传感器节点的连续运行。但是,当需要处理大量数据时,节点可能会因为无法及时充电而进入异常死亡状态。因此,节点死亡概率的预测对于指导充电车辆的充电路径规划至关重要。在本文中,我们建立了一个基于马尔可夫流体队列(MFQ)模型的分析模型,旨在创建节点的收获-储存-使用(HSU)和收获后使用(HTU)模型。具体而言,所提出的模型涉及马尔可夫过程、排队模型和连续流体过程。结果表明,模型计算出的异常死亡概率与模拟得到的死亡概率相差约0.1%。同时,通过比较两种能源使用模式,我们发现HTU优于HSU。

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