Research on Task Offloading Based on Deep Reinforcement Learning for Internet of Vehicles
Authors: 
Yaoping Zeng, Yanwei Hu, Ting YangAuthors Info & Claims
Yaoping Zeng, Yanwei Hu, Ting YangAuthors Info & ClaimsPages 1033 - 1038
Abstract
Mobile Edge Computing (MEC) is a promising technology that facilitates the computational offloading and resource allocation in the Internet of Vehicles (IoV) environment. When the mobile device is not capable enough to meet its own demands for data processing, the task will be offloaded to the MEC server, which can effectively relieve the network pressure, meet the multi-task computing requirements, and ensure the quality of service (QoS). Via multi-user and multi-MEC servers, this paper proposes the Q-Learning task offloading strategy based on the improved deep reinforcement learning policy(IDRLP) to obtain an optimal strategy for task offloading and resource allocation. Simulation results suggest that the proposed algorithm compared with other benchmark schemes has better performance in terms of delay, energy consumption and system weighted cost, even with different tasks, users and data sizes.
References
[1]
Q Yuan, H Zhou and J Li. 2018. Toward Efficient Content Delivery for Automated Driving Services: An Edge Computing Solution. IEEE Network. 32, 1, 80-86.
[2]
J Zhao, Q Li and Y Gong. 2019. Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks. IEEE Transactions on Vehicular Technology. 68, 8, 7944-7956.
[3]
Y Wang, K Wang and H Huang. 2018. Traffic and Computation Co-Offloading With Reinforcement Learning in Fog Computing for Industrial Applications. IEEE Transactions on Industrial Informatics. 1-1.
[4]
K Wang, X Wang and X Liu. 2020. Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles. IEEE Access. 8, 173779-173789.
[5]
F Jiang, W Liu, J Wang and X Liu. 2020. Q-Learning Based Task Offloading and Resource Allocation Scheme for Internet of Vehicles. IEEE/CIC International Conference on Communications in China (ICCC). 460-465.
[6]
M Li, J Gao, L Zhao and X Shen. 2020. Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks. IEEE Transactions on Cognitive Communications and Networking. 6, 4, 1122-1135.
[7]
Z Yu, Y Tang, L Zhang and H Zeng. 2021. Deep Reinforcement Learning Based Computing Offloading Decision and Task Scheduling in Internet of Vehicles. IEEE/CIC International Conference on Communications in China (ICCC). 1166-1171.
[8]
Z Wu and D Yan. 2021. Deep reinforcement learning-based computation offloading for 5G vehicle-aware multi-access edge computing network. China Communications. 18, 11, 26-41.
[9]
X Gu, G Zhang and N Zhao. 2020. Cooperative Mobile Edge Computing Architecture in IoV and Its Workload Balance Policy[C]. IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT).
[10]
X Chen, Y Cai and L Li. 2019. Energy-Efficient Resource Allocation for Latency-Sensitive Mobile Edge Computing[J]. IEEE Transactions on Vehicular Technology. 1-1.
[11]
Y Cui, D Zhang and T Zhang. 2021. A New Approach on Task Offloading Scheduling for Application of Mobile Edge Computing[C]. IEEE Wireless Communications and Networking Conference (WCNC).
[12]
W Duan, X Gu, M Wen, Y Ji, J Ge and G Zhang. 2021. Resource Management for Intelligent Vehicular Edge Computing Networks. IEEE Transactions on Intelligent Transportation Systems.
[13]
X Gao and F Xu. 2020. Research on task offloading based on deep reinforcement learning in mobile edge environment[J]. MATEC Web of Conferences.
[14]
K Jiang, H Zhou and D L0.i 2020. A Q-learning based Method for Energy-Efficient Computation Offloading in Mobile Edge Computing[C]. International Conference on Computer Communications and Networks (ICCCN).
Index Terms
- Research on Task Offloading Based on Deep Reinforcement Learning for Internet of Vehicles
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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