Collaborative Learning of Communication Routes in Edge-Enabled Multi-Access Vehicular Environment

• Published in: IEEE transactions on cognitive communications and networking Vol. 6; no. 4; pp. 1155 - 1165
• Main Authors: Wu, Celimuge, Liu, Zhi, Liu, Fuqiang, Yoshinaga, Tsutomu, Ji, Yusheng, Li, Jie
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Caching; Collaborative learning; Communication; Delays; Edge computing; fuzzy logic; Internet of Things; Machine learning; multi-access vehicular environment; Preempting; Quality of service; Reinforcement learning; Resource management; Routing; routing protocol; Task analysis; Vehicular networks
• ISSN: 2332-7731; 2332-7731
• DOI: 10.1109/TCCN.2020.3002253

Some 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.