Collaborative Learning of Communication Routes in Edge-Enabled Multi-Access Vehicular Environment
Collaborative Learning of Communication Routes in Edge-Enabled Multi-Access Vehicular Environment
复制标题
边缘使能多通道车载环境中通信路径的协作学习
DOI：
10.1109/tccn.2020.3002253
复制
发表时间：
2020-12-01
影响因子：
8.6
通讯作者：
Li, Jie
中科院分区：
文献类型：
--
作者：
Wu, Celimuge;Liu, Zhi;Li, Jie
一些物联网（IoT）应用对端到端延迟有严格的要求，其中边缘计算可以用于通过在边缘节点处进行高效的缓存和计算来为最终用户提供短延迟。然而，快速和有效的通信路径创建在多路访问车辆环境是一个未充分探索的研究问题。在本文中，我们提出了一种基于协作学习的多路访问车辆边缘计算环境的路由方案。该方案采用基于端-边-云协作的强化学习算法，以低通信开销的主动方式找到路由。还基于所学习的信息抢先改变路由。通过整合“主动”和“抢占”的方法，所提出的方案可以实现更好的转发数据包相比，现有的替代方案。我们进行了广泛的和现实的计算机模拟，以显示所提出的计划比现有的基线的性能优势。
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.