Explore Deep Reinforcement Learning to Energy-Efficient Data Synchronism in 5G Self-Powered Sensor Networks

doi:10.1109/JSEN.2022.3221983

CQUET-IR > 智慧健康学院

	Explore Deep Reinforcement Learning to Energy-Efficient Data Synchronism in 5G Self-Powered Sensor Networks
	Wu, Chunyi1 ; Zhao, Yanhua 2; Xiao, Shan1 ; Gao, Chao1
	2023-09-15
发表期刊	IEEE SENSORS JOURNAL
ISSN	1530-437X
EISSN	1558-1748
卷号	23 期号:18 页码:20586-20595
摘要	Recently, the explosive data traffic growth and large-scale Internet of Things (IoT) equipment connection have brought 5G mobile communication technology many challenges. The communication delay of network services such as augmented reality, smart grid, disaster warning, and emergency communication has been relieved by 5G. However, these services are still confronted with serious challenge of energy conservation. Most of the traditional optimization methods usually need complex operations and iterations to get optimal results, which are not suitable for communication systems with high real-time performance. Based on this argument, this article has focused on green communication in mobile edge computing (MEC)-based self-powered sensor network and proposed a novel approach called mobile intelligent data synchronization [MIDS based on deep reinforcement learning (DLR)]. It exploits deep deterministic policy gradient (DDPG), continuously interacting with the environment and making tryouts to evaluate the feedback from the environment to optimize future decision-making on the path selection scheme for data transmission as well as achieve the energy efficiency and energy consumption balance of mobile devices with self-powered sensors. We provide experiments to show the capability of our approach in reducing the additional energy consumption of mobile devices and the base station (BS) as well as balancing the energy consumption of mobile devices with sensor communication. The superiority of our approach for energy conservation in data synchronization is convincingly demonstrated by comparing it with state-of-the-art methods in MEC-based self-powered sensor network. © 2001-2012 IEEE.
关键词	5G mobile communication systems Augmented reality Decision making Deep learning Energy efficiency Energy utilization Internet of things Mobile edge computing Real time systems Sensor networks Synchronization Data synchronization Data traffic growth Deep reinforcement learning Emergency communication Energy efficient Energy-consumption Reinforcement learnings Self-powered Self-powered sensor Sensors network
DOI	10.1109/JSEN.2022.3221983
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Engineering ; Instruments & Instrumentation ; Physics
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied
WOS记录号	WOS:001090399700012
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20225113274231
原始文献类型	Journal article (JA)
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/18054
专题	智慧健康学院人工智能与大数据学院电子与物联网学院
作者单位	1.Big Data and Optimization Research Institute, Chongqing College of Electronic Engineering, Chongqing; 401331, China; 2.Chongqing No.1 Middle School, Chongqing; 401331, China
第一作者单位	重庆电子科技职业大学
推荐引用方式 GB/T 7714	Wu, Chunyi,Zhao, Yanhua,Xiao, Shan,et al. Explore Deep Reinforcement Learning to Energy-Efficient Data Synchronism in 5G Self-Powered Sensor Networks[J]. IEEE SENSORS JOURNAL,2023,23(18):20586-20595.
APA	Wu, Chunyi,Zhao, Yanhua,Xiao, Shan,&Gao, Chao.(2023).Explore Deep Reinforcement Learning to Energy-Efficient Data Synchronism in 5G Self-Powered Sensor Networks.IEEE SENSORS JOURNAL,23(18),20586-20595.
MLA	Wu, Chunyi,et al."Explore Deep Reinforcement Learning to Energy-Efficient Data Synchronism in 5G Self-Powered Sensor Networks".IEEE SENSORS JOURNAL 23.18(2023):20586-20595.