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