A Collaborative Mechanism for Smart Highway Edge Tasks Based on Location Prediction

SHAO Sujie; CHAI Ruijun; GUO Shaoyong; WU Shuang; WANG Zhili; QIU Xuesong

doi:10.11999/JEIT220279

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1154-1162

SHAO Sujie, CHAI Ruijun, GUO Shaoyong, WU Shuang, WANG Zhili, QIU Xuesong. A Collaborative Mechanism for Smart Highway Edge Tasks Based on Location Prediction[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1154-1162. doi: 10.11999/JEIT220279

Citation:

SHAO Sujie, CHAI Ruijun, GUO Shaoyong, WU Shuang, WANG Zhili, QIU Xuesong. A Collaborative Mechanism for Smart Highway Edge Tasks Based on Location Prediction[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1154-1162. doi: 10.11999/JEIT220279

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A Collaborative Mechanism for Smart Highway Edge Tasks Based on Location Prediction

doi: 10.11999/JEIT220279

1.
State Key Laboratory Of Networking And Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
State Grid Ningxia Electric Power Co,. Ltd, Yinchuan 750001, China

Funds: The National Natural Science Foundation of China (62071070), The Key Project Plan of Blockchain in Ministry of Education of the People’s Republic of China (KJ010802)

Received Date: 2022-03-14
Rev Recd Date: 2022-06-16

Available Online: 2022-06-21

Publish Date: 2023-04-10

Abstract

Abstract

In recent years new services such as road monitoring and assisted driving in smart highways have been proposed, but the explosive growth of data traffic has also emerged, which has brought a great test to the carrying capacity of the network. With the maturity of 5G and mobile edge computing technology, massive tasks do not have to be processed centrally in the cloud, and edge-side co-processing becomes a better choice. In order to provide efficient and reliable services for users in the vehicle high-speed mobile scenario, a Collaboration of Edge Tasks based on Location Prediction (CETLP) is proposed in this paper. First, a delay and load balancing-oriented edge task collaboration model is established by combining the vehicle movement characteristics in the smart highway scenario. Then, a deep reinforcement learning-based edge task collaboration algorithm is proposed to solve the collaboration strategy for a large number of tasks with the objectives of task delay minimization and network load balancing. Simulation results show that the proposed mechanism can reduce the service delay while ensuring the network load balancing.
- Mobile Edge Computing(MEC),
- Edge collaboration,
- Deep Reinforcement Learning(DRL),
- Smart highway,
- Location prediction

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References(14)

References

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