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.

中文翻译：

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支持边缘的多路访问车载环境中通信路由的协作学习

一些物联网（IoT）应用对端到端的延迟有严格的要求，边缘计算可以通过在边缘节点进行高效的缓存和计算来为最终用户提供较短的延迟。然而，在多路访问车辆环境中快速有效的通信路由创建是一个未充分探索的研究问题。在本文中，我们为多路访问车辆边缘计算环境提出了一种基于协作学习的路由方案。所提出的方案采用基于端-边-云协作的强化学习算法，以低通信开销的主动方式寻找路由。路由也会根据学习到的信息抢先改变。通过整合“主动”和“先发制人”的方法，与现有替代方案相比，所提出的方案可以实现更好的数据包转发。我们进行了广泛而真实的计算机模拟，以显示所提出方案相对于现有基线的性能优势。