The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0
Abstract
Industry 4.0 aims to create a modern industrial system by introducing technologies, such as cloud computing, intelligent robotics, and wireless sensor networks. In this article, we consider the multichannel access and task offloading problem in mobile-edge computing (MEC)-enabled industry 4.0 and describe this problem in multiagent environment. To solve this problem, we propose a novel multiagent deep reinforcement learning (MADRL) scheme. The solution enables edge devices (EDs) to cooperate with each other, which can significantly reduce the computation delay and improve the channel access success rate. Extensive simulation results with different system parameters reveal that the proposed scheme could reduce computation delay by 33.38% and increase the channel access success rate by 14.88% and channel utilization by 3.24% compared to the traditional single-agent reinforcement learning method.
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Cites
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Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630
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Learning to Communicate with Deep Multi-Agent Reinforcement Learning
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Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control
Chu, T., Wang, J., Codeca, L., & Li, Z. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2901791 · Cited by 1,046
Learning with Opponent-Learning Awareness
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Cited by
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey
Azari, M. M., Solanki, S., Chatzinotas, S., Kodheli, O., Sallouha, H., Colpaert, A., Mendoza Montoya, J. F., Pollin, S., Haqiqatnejad, A., Mostaani, A., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3199901 · Cited by 668
Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Feriani, A., & Hossain, E. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3063822 · Cited by 410
Towards edge computing in intelligent manufacturing: Past, present and future
Nain, G., Pattanaik, K. K., & Sharma, G. K. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.01.010 · Cited by 283
A Survey on Task Offloading in Multi-access Edge Computing
Islam, A., Debnath, A., Ghose, M., & Chakraborty, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102225 · Cited by 271
Deng, X., Yin, J., Guan, P., Xiong, N. N., Zhang, L., & Mumtaz, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3123406 · Cited by 267
Metadata
- Authors
- Cao, Z., Zhou, P., Li, R., Huang, S., & Wu, D.
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-07
- Volume
- 7
- Issue
- 7
- Pages
- 6201-6213
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3011182939
- Cited by
- 183
- References
- 7
- FWCI
- 12.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cao, Z., Zhou, P., Li, R., Huang, S., & Wu, D. (2020). Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2020.2968951
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