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
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ISSN/eISSN | 2473-2400 |
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 |
DOI | 10.1109/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 | |
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. |
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