科研成果详情

发表状态已发表Published
题名Trust-Based Multi-Agent Imitation Learning for Green Edge Computing in Smart Cities
作者
发表日期2022-09-01
发表期刊IEEE Transactions on Green Communications and Networking
ISSN/eISSN2473-2400
卷号6期号:3页码:1635-1648
摘要

Green communications and networking technologies boost the interconnection and communication of Internet of Things (IoT) devices, so as to facilitate the task offloading. Artificial Intelligence (AI) based task offloading scheme is being widely studied. However, most of AI based task offloading schemes only reward the devices that process tasks locally, and do not consider the untrusted devices. To solve these issues, a Trust based Multi-Agent Imitation Learning (T-MAIL) scheme is proposed by us to improve task offloading for edge computing in smart cities. Firstly, we established a full task offloading incentive model, in which edge devices can get comprehensive reward from local processing and task re-offloading. Secondly, we proposed an active trust acquisition method, which can obtain the device trust efficiently and accurately. Finally, the new task offloading incentive scheme and trust acquisition method are introduced into multi-agent imitation learning. The experimental results show that, the proposed T-MAIL will effectively improve task offloading. Compared with MILP and DQN based task offloading solution, the average task completion time is reduced by 5.5% and 52.7% respectively. Compared with MILP scheme, the task offloading rate is increased by 19.2%. In addition, the trust difference ratio between trusted devices and untrusted devices can reach 56.1%.

关键词Artificial intelligence green edge computing imitation learning task offloading trust computing
DOI10.1109/TGCN.2022.3172367
URL查看来源
收录类别SCIE
语种英语English
WOS研究方向Telecommunications
WOS类目Telecommunications
WOS记录号WOS:000842063800039
Scopus入藏号2-s2.0-85129632682
引用统计
被引频次:20[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/10587
专题个人在本单位外知识产出
通讯作者Zhu, Chunsheng
作者单位
1.Central South University,School of Computer Science and Engineering,Changsha,410083,China
2.Shenzhen Technology University,College of Big Data and Internet,Shenzhen,518118,China
3.Beijing Normal University & UIC,School of Artificial Intelligence and Future Networks,Zhuhai,519087,China
4.Hunan University of Science and Technology,School of Computer Science and Engineering,Xiangtan,411201,China
推荐引用方式
GB/T 7714
Zeng, Pengjie,Liu, Anfeng,Zhu, Chunshenget al. Trust-Based Multi-Agent Imitation Learning for Green Edge Computing in Smart Cities[J]. IEEE Transactions on Green Communications and Networking, 2022, 6(3): 1635-1648.
APA Zeng, Pengjie, Liu, Anfeng, Zhu, Chunsheng, Wang, Tian, & Zhang, Shaobo. (2022). Trust-Based Multi-Agent Imitation Learning for Green Edge Computing in Smart Cities. IEEE Transactions on Green Communications and Networking, 6(3), 1635-1648.
MLA Zeng, Pengjie,et al."Trust-Based Multi-Agent Imitation Learning for Green Edge Computing in Smart Cities". IEEE Transactions on Green Communications and Networking 6.3(2022): 1635-1648.
条目包含的文件
条目无相关文件。
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[Zeng, Pengjie]的文章
[Liu, Anfeng]的文章
[Zhu, Chunsheng]的文章
百度学术
百度学术中相似的文章
[Zeng, Pengjie]的文章
[Liu, Anfeng]的文章
[Zhu, Chunsheng]的文章
必应学术
必应学术中相似的文章
[Zeng, Pengjie]的文章
[Liu, Anfeng]的文章
[Zhu, Chunsheng]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。