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Edge Computation Offloading With Content Caching in 6G-Enabled IoV

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Edge Computation Offloading With Content Caching in 6G-Enabled IoV. / Zhou, Xuanhong; Bilal, Muhammad; Dou, Ruihan et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 3, 01.03.2024, p. 2733 - 2747.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Zhou, X, Bilal, M, Dou, R, Rodrigues, JJPC, Zhao, Q, Dai, J & Xu, X 2024, 'Edge Computation Offloading With Content Caching in 6G-Enabled IoV', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 3, pp. 2733 - 2747. https://doi.org/10.1109/TITS.2023.3239599

APA

Zhou, X., Bilal, M., Dou, R., Rodrigues, J. J. P. C., Zhao, Q., Dai, J., & Xu, X. (2024). Edge Computation Offloading With Content Caching in 6G-Enabled IoV. IEEE Transactions on Intelligent Transportation Systems, 25(3), 2733 - 2747. https://doi.org/10.1109/TITS.2023.3239599

Vancouver

Zhou X, Bilal M, Dou R, Rodrigues JJPC, Zhao Q, Dai J et al. Edge Computation Offloading With Content Caching in 6G-Enabled IoV. IEEE Transactions on Intelligent Transportation Systems. 2024 Mar 1;25(3):2733 - 2747. Epub 2023 Jan 31. doi: 10.1109/TITS.2023.3239599

Author

Zhou, Xuanhong ; Bilal, Muhammad ; Dou, Ruihan et al. / Edge Computation Offloading With Content Caching in 6G-Enabled IoV. In: IEEE Transactions on Intelligent Transportation Systems. 2024 ; Vol. 25, No. 3. pp. 2733 - 2747.

Bibtex

@article{893d29f05d9f4ecd8403a7e102427cfe,
title = "Edge Computation Offloading With Content Caching in 6G-Enabled IoV",
abstract = "Using the powerful communication capability of 6G, various in-vehicle services in the Internet of Vehicles (IoV) can be offered with low delay, which provide users with a high-quality driving experience. Edge computing in 6G-enabled IoV utilizes edge servers distributed at the edge of the road, enabling rapid responses to delay-sensitive tasks. However, how to execute computation offloading effectively in 6G-enabled IoV remains a challenge. In this paper, a Computation Offloading method with Demand prediction and Reinforcement learning, named CODR, is proposed. First, a prediction method based on Spatial-Temporal Graph Neural Network (STGNN) is proposed. According to the predicted demand, a caching decision method based on the simplex algorithm is designed. Then, a computation offloading method based on twin delayed deterministic policy gradient (TD3) is proposed to obtain the optimal offloading scheme. Finally, the effectiveness and superiority of CODR in reducing delay are demonstrated through a large number of simulation experiments.",
keywords = "6G, 6G mobile communication, caching, computation offloading, Delays, Edge computing, edge computing, Internet of Vehicles, reinforcement learning, Servers, Task analysis, Vehicle dynamics",
author = "Xuanhong Zhou and Muhammad Bilal and Ruihan Dou and Rodrigues, {Joel J.P.C.} and Qingzhan Zhao and Jianguo Dai and Xiaolong Xu",
year = "2024",
month = mar,
day = "1",
doi = "10.1109/TITS.2023.3239599",
language = "English",
volume = "25",
pages = "2733 -- 2747",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Edge Computation Offloading With Content Caching in 6G-Enabled IoV

AU - Zhou, Xuanhong

AU - Bilal, Muhammad

AU - Dou, Ruihan

AU - Rodrigues, Joel J.P.C.

AU - Zhao, Qingzhan

AU - Dai, Jianguo

AU - Xu, Xiaolong

PY - 2024/3/1

Y1 - 2024/3/1

N2 - Using the powerful communication capability of 6G, various in-vehicle services in the Internet of Vehicles (IoV) can be offered with low delay, which provide users with a high-quality driving experience. Edge computing in 6G-enabled IoV utilizes edge servers distributed at the edge of the road, enabling rapid responses to delay-sensitive tasks. However, how to execute computation offloading effectively in 6G-enabled IoV remains a challenge. In this paper, a Computation Offloading method with Demand prediction and Reinforcement learning, named CODR, is proposed. First, a prediction method based on Spatial-Temporal Graph Neural Network (STGNN) is proposed. According to the predicted demand, a caching decision method based on the simplex algorithm is designed. Then, a computation offloading method based on twin delayed deterministic policy gradient (TD3) is proposed to obtain the optimal offloading scheme. Finally, the effectiveness and superiority of CODR in reducing delay are demonstrated through a large number of simulation experiments.

AB - Using the powerful communication capability of 6G, various in-vehicle services in the Internet of Vehicles (IoV) can be offered with low delay, which provide users with a high-quality driving experience. Edge computing in 6G-enabled IoV utilizes edge servers distributed at the edge of the road, enabling rapid responses to delay-sensitive tasks. However, how to execute computation offloading effectively in 6G-enabled IoV remains a challenge. In this paper, a Computation Offloading method with Demand prediction and Reinforcement learning, named CODR, is proposed. First, a prediction method based on Spatial-Temporal Graph Neural Network (STGNN) is proposed. According to the predicted demand, a caching decision method based on the simplex algorithm is designed. Then, a computation offloading method based on twin delayed deterministic policy gradient (TD3) is proposed to obtain the optimal offloading scheme. Finally, the effectiveness and superiority of CODR in reducing delay are demonstrated through a large number of simulation experiments.

KW - 6G

KW - 6G mobile communication

KW - caching

KW - computation offloading

KW - Delays

KW - Edge computing

KW - edge computing

KW - Internet of Vehicles

KW - reinforcement learning

KW - Servers

KW - Task analysis

KW - Vehicle dynamics

U2 - 10.1109/TITS.2023.3239599

DO - 10.1109/TITS.2023.3239599

M3 - Journal article

AN - SCOPUS:85148445603

VL - 25

SP - 2733

EP - 2747

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 3

ER -