loading page

Collaborative Learning of Communication Routes in Edge-enabled Multi-access Vehicular Environment
  • +3
  • Celimuge Wu ,
  • Zhi Liu ,
  • Tsutomu Yoshinaga ,
  • Fuqiang Liu ,
  • Yusheng Ji ,
  • Jie Li
Celimuge Wu
The University of Electro-Communications, The University of Electro-Communications

Corresponding Author:[email protected]

Author Profile
Tsutomu Yoshinaga
Author Profile
Fuqiang Liu
Author Profile
Yusheng Ji
Author Profile


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.
Dec 2020Published in IEEE Transactions on Cognitive Communications and Networking volume 6 issue 4 on pages 1155-1165. 10.1109/TCCN.2020.3002253