基于深度强化学习的物联网环境感知无线移动充电方案

Michaël Mahamat, Ghada Jaber, A. Bouabdallah
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引用次数: 2

摘要

多年来,物联网(IoT)越来越受欢迎,并在许多应用中得到了应用。物联网网络采用许多受限设备,因此,寻找能量是必须的,以最大限度地提高设备和网络的使用寿命。本文研究了一种基于无线移动充电器(MCs)的设备寿命最大化方案。我们设计了一种考虑到设备近期需求的充电方案,而不是将能量传输给设备然后只给它们充电。我们正在研究一种情境感知无线能量传输方案,根据当前和可能即将发生的事件为设备充电。我们的方案基于两个模块:预测物联网网络中可能的未来事件的上下文推理模块和使用深度强化学习(DRL)的智能无线移动充电器。我们的解决方案旨在建立一个预防性收费方案,考虑能源状况和未来可能发生的事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Deep Reinforcement Learning-Based Context-Aware Wireless Mobile Charging Scheme for the Internet of Things
The Internet of Things (IoT) has gained in popularity over the years and is used in numerous applications. IoT networks employ many constrained devices, thus, finding energy is mandatory to maximize device and network lifetime. In this paper, we investigate a scheme based on wireless Mobile Chargers (MCs) to maximize device lifetime. Instead of transmitting energy to devices to only charge them back, we design a charging scheme considering the near future needs of the devices. We provide our ongoing research on a context-aware wireless energy transfer scheme to charge the devices according to the current and probable upcoming events. Our scheme is based on two modules: a context reasoning module predicting the possible future events in the IoT network and an intelligent Wireless Mobile Charger using Deep Reinforcement Learning (DRL). Our solution aims to establish a preventive charging scheme, considering the energy status and probable future events.
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