The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems
Open accessAbstract
The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.
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Cites
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A Survey on Mobile Edge Computing: The Communication Perspective
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Efficient multi-user computation offloading for mobile-edge cloud computing
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Dedicated Short-Range Communications (DSRC) Standards in the United States
Kenney, J. B. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2132790 · Cited by 2,366
A Survey on the Edge Computing for the Internet of Things
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“manage radio and computing resources to execute tasks, which has been widely investigated recently, see surveys [4]–[6] and technical papers [7]–[9].”
BackgroundEnergy-Efficient Resource Allocation for Mobile-Edge Computation Offloading
You, C., Huang, K., Chae, H., & Kim, B.-H. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2016.2633522 · Cited by 1,503
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
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Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel
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Cited by
Liu, L., Zhao, M., Yu, M., Jan, M. A., Lan, D., & Taherkordi, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3142566 · Cited by 303
Computation offloading in mobile edge computing networks: A survey
Feng, C., Han, P., Zhang, X., Yang, B., Liu, Y., & Guo, L. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103366 · Cited by 284
UAV-Assisted Task Offloading in Vehicular Edge Computing Networks
Dai, X., Xiao, Z., Jiang, H., & Lui, J. C. S. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3259394 · Cited by 284
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
- Sun, Y., Guo, X., Song, J., Zhou, S., Jiang, Z., Liu, X., & Niu, Z.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2019-04
- Volume
- 68
- Issue
- 4
- Pages
- 3061-3074
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2964221923
- Cited by
- 369
- References
- 25
- FWCI
- 25.89
- Type
- journal article
- Language
- en
- Open access
- green
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