Deep Reinforcement Learning for RIS-Aided Secure Mobile Edge Computing in Industrial Internet of Things

doi:10.1109/TII.2023.3292968

	Deep Reinforcement Learning for RIS-Aided Secure Mobile Edge Computing in Industrial Internet of Things
	Xu, Jianpeng 1; Xu, Aoshuo 1; Chen, Liangyu 2; Chen, Yali 3; Liang, Xiaolin 1; Ai, Bo 4,5,6,7
	2024-02-01
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN	1551-3203
卷号	20 期号:2 页码:2455-2464
摘要	Mobile edge computing (MEC) has been regarded as a promising paradigm to support the compute-intensive and delay-sensitive industrial Internet of things (IIoT) applications. However, the nature of broadcasting in wireless communications may cause that the task offloading security is easy to be threatened from eavesdroppers. Aiming at improving the task offloading security, this article studies the benefit of deploying the emerging reconfigurable intelligent surface (RIS) in MEC-enabled IIoT networks with eavesdroppers, and forms the RIS-aided secure MEC system with time-division multiple access. In addition, we formulate a joint RIS phase shift, power control, local computation rate, and time-slot allocation optimization problem to maximize the weighted sum secrecy computation efficiency (WSSCE) among IIoT devices. To address this intractable problem, we propose a deep reinforcement learning (DRL)-based algorithm, where a deep deterministic policy gradient (DDPG) agent is adopted. Numerical results demonstrate that 1) deploying the RIS can improve the WSSCE performance; 2) the proposed DDPG-based algorithm can obtain higher WSSCE than other baseline methods.
关键词	Industrial Internet of Things Resource management Task analysis Wireless communication Power control Energy consumption Mathematical models Deep reinforcement learning (DRL) industrial Internet of things (IIoT) mobile edge computing (MEC) reconfigurable intelligent surface (RIS) secure offloading
DOI	10.1109/TII.2023.3292968
收录类别	SCI
语种	英语
资助项目	High-Level Talents Research Start-Up Project of Hebei University
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:001171888600173
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39013
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Jianpeng; Ai, Bo
作者单位	1.Hebei Univ, Coll Elect & Informat Engn, Baoding 071002, Peoples R China 2.Huawei Technol, Beijing 100095, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China 4.Beijing Jiaotong Univ, Sch Elect & Informat Engn, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China 5.Beijing Jiaotong Univ, Frontiers Sci Ctr Smart High Speed Railway Syst, Beijing 100044, Peoples R China 6.Peng Cheng Lab, Res Ctr Networks & Commun, Shenzhen 518055, Peoples R China 7.Zhengzhou Univ, Henan Joint Int Res Lab Intelligent Networking & D, Zhengzhou, Peoples R China
推荐引用方式 GB/T 7714	Xu, Jianpeng,Xu, Aoshuo,Chen, Liangyu,et al. Deep Reinforcement Learning for RIS-Aided Secure Mobile Edge Computing in Industrial Internet of Things[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2024,20(2):2455-2464.
APA	Xu, Jianpeng,Xu, Aoshuo,Chen, Liangyu,Chen, Yali,Liang, Xiaolin,&Ai, Bo.(2024).Deep Reinforcement Learning for RIS-Aided Secure Mobile Edge Computing in Industrial Internet of Things.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,20(2),2455-2464.
MLA	Xu, Jianpeng,et al."Deep Reinforcement Learning for RIS-Aided Secure Mobile Edge Computing in Industrial Internet of Things".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20.2(2024):2455-2464.