The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System
Abstract
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
You, X., Wang, C.-X., Huang, J., Gao, X., Zhang, Z., Wang, M., Huang, Y., Zhang, C., Jiang, Y., Wang, J., et al. (2021) · Science China Information Sciences · doi:10.1007/s11432-020-2955-6 · Cited by 2,027
Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning
Wang, J., Hu, J., Min, G., Zomaya, A. Y., & Georgalas, N. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3014896 · Cited by 414
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Self-citation
Metadata
- Authors
- Ning, Z., Dong, P., Wang, X., Rodrigues, J. J. P. C., & Xia, F.
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2019-10-18
- Volume
- 10
- Issue
- 6
- Pages
- 1-24
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W2980360843
- Cited by
- 292
- References
- 36
- FWCI
- 26.02
- Type
- journal article
- Language
- en
- Open access
- closed
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