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Collaborative Learning of Communication Routes in Edge-enabled Multi-access Vehicular Environment

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posted on 15.06.2020, 15:11 by Celimuge Wu, Zhi Liu, Fuqiang Liu, Tsutomu Yoshinaga, Yusheng Ji, Jie Li
Some vehicular Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the ``proactive'' and ``preemptive'' approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.

History

Email Address of Submitting Author

celimuge@uec.ac.jp

ORCID of Submitting Author

0000-0001-6853-5878

Submitting Author's Institution

The University of Electro-Communications

Submitting Author's Country

Japan

Licence

Exports