Deep Reinforcement Learning for Dynamic Access Control with Battery Prediction for Mobile-Edge Computing in Green IoT Networks

Lijuan Xu, Meng Qin, Qinghai Yang, K. Kwak
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引用次数: 7

Abstract

Mobile Edge Computing (MEC) technology has emerged as a promising paradigm to reduce the energy consumption for the resource-constrained and energy-limited Internet of Things (IoT) networks. In this paper, benefiting from energy harvesting technique (EH), we study the dynamic MEC-access control problem for maximizing the long-term average uplink transmission rate whilst minimizing the transmission energy consumption for green IoT networks, in which the IoT device is powered by a rechargeable battery that can harvest energy from the surrounding environments. In particular, this problem is formulated as a Markov decision process with system dynamics unknown. On accounting of the dynamics of the wireless channel state, the energy arrival, and the mobility of the IoT device, a Long Short-Term Memory (LSTM) enhanced Deep Q-Network (DQN) based (LSDQN) access control algorithm is proposed for the IoT network. In the proposed algorithm, the LSTM model is used to predict the battery status for assisting the IoT device to determine the optimal access control decision by DQN with the target of maximizing the average uplink rate whilst minimizing the energy consumption. Finally, extensive simulation results verify the performance of the proposed algorithm.
绿色物联网移动边缘计算中电池预测动态访问控制的深度强化学习
移动边缘计算(MEC)技术已经成为一种有前途的范例,可以减少资源受限和能源有限的物联网(IoT)网络的能耗。在本文中,利用能量收集技术(EH),我们研究了动态mec访问控制问题,以最大化长期平均上行传输速率,同时最小化绿色物联网网络的传输能耗,其中物联网设备由可从周围环境中收集能量的可充电电池供电。特别地,这个问题被表述为一个系统动力学未知的马尔可夫决策过程。考虑到无线信道状态的动态、能量到达和物联网设备的移动性,提出了一种基于LSTM增强深度Q-Network (DQN)的物联网网络(LSDQN)访问控制算法。在该算法中,利用LSTM模型预测电池状态,帮助物联网设备确定DQN的最优接入控制决策,以最大平均上行速率和最小能耗为目标。最后,大量的仿真结果验证了所提算法的性能。
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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