The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Reducing Offloading Latency for Digital Twin Edge Networks in 6G
Abstract
6G is envisioned to empower wireless communication and computation through the digitalization and connectivity of everything, by establishing a digital representation of the real network environment. Mobile edge computing (MEC), as one of the key enabling factors, meets unprecedented challenges during mobile offloading due to the extremely complicated and unpredictable network environment in 6G. The existing works on offloading in MEC mainly ignore the effects of user mobility and the unpredictable MEC environment. In this paper, we present a new vision of Digital Twin Edge Networks (DITEN) where digital twins (DTs) of edge servers estimate edge servers' states and DT of the entire MEC system provides training data for offloading decision. A mobile offloading scheme is proposed in DITEN to minimize the offloading latency under the constraints of accumulated consumed service migration cost during user mobility. The Lyapunov optimization method is leveraged to simplify the long-term migration cost constraint to a multi-objective dynamic optimization problem, which is then solved by Actor-Critic deep reinforcement learning. Simulations results show that our proposed scheme effectively diminishes the average offloading latency, the offloading failure rate, and the service migration rate, as compared with benchmark schemes, while saving the system cost with DT assistance.
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Cites
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
Mach, P., & Becvar, Z. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2682318 · Cited by 3,258
Mobile Edge Computing: A Survey
Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2750180 · Cited by 2,474
Self-citationThe Roadmap to 6G: AI Empowered Wireless Networks
Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y.-J. A. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1900271 · Cited by 2,207
Self-citationDynamic computation offloading for mobile-edge computing with energy harvesting devices
Mao, Y., Zhang, J., & Letaief, K. B. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2611964 · Cited by 1,705
T-drive: driving directions based on taxi trajectories
Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., & Huang, Y. (2010) · Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems · doi:10.1145/1869790.1869807 · Cited by 1,105
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
Grondman, I., Busoniu, L., Lopes, G. A. D., & Babuska, R. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2218595 · Cited by 1,042
The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0
Uhlemann, T. H.-J., Lehmann, C., & Steinhilper, R. (2017) · Procedia CIRP · doi:10.1016/j.procir.2016.11.152 · Cited by 994
Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling
Thinh, T. Q., Tang, J., La, Q. D., & Quek, T. Q. S. (2017) · IEEE Transactions on Communications · doi:10.1109/tcomm.2017.2699660 · Cited by 866
Viswanathan, H., & Mogensen, P. E. (2020) · IEEE Access · doi:10.1109/access.2020.2981745 · Cited by 727
A Digital Twin Based Industrial Automation and Control System Security Architecture
Gehrmann, C., & Gunnarsson, M. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2938885 · Cited by 353
Cited by
Digital Twin Networks: A Survey
Wu, Y., Zhang, K., & Zhang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3079510 · Cited by 847
Self-citationWang, Y., Su, Z., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263909 · Cited by 357
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304
Blockchain and Federated Learning for Collaborative Intrusion Detection in Vehicular Edge Computing
Liu, H., Zhang, S., Zhang, P., Zhou, X., Shao, X., Pu, G., & Zhang, Y. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3076780 · Cited by 291
Self-citationZhou, Z., Jia, Z., Liao, H., Lu, W., Mumtaz, S., Guizani, M., & Tariq, M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3137349 · Cited by 169
Metadata
- Authors
- Sun, W., Zhang, H., Wang, R., & Zhang, Y.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2020-10
- Volume
- 69
- Issue
- 10
- Pages
- 12240-12251
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W3080667742
- Cited by
- 373
- References
- 26
- FWCI
- 22.75
- Type
- journal article
- Language
- en
- Open access
- closed
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