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