Inner Mongolia Normal University, Hohhot 010010, China.
Department of Computer and Network Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan.
Sensors (Basel). 2018 Jun 24;18(7):2022. doi: 10.3390/s18072022.
We propose a context-aware edge-based packet forwarding scheme for vehicular networks. The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized manner, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. A reinforcement learning algorithm is used to optimize the last two-hop communications in order to improve the adaptiveness of the communication routes. The proposed scheme selects different edge nodes for different types of communications with different context information such as connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size. We launch extensive simulations to evaluate the proposed scheme by comparing with existing broadcast protocols and unicast protocols for various network conditions and traffic patterns.
我们提出了一种面向车联网的基于上下文感知的边缘包转发方案。该方案采用基于模糊逻辑的边缘节点选择协议，以分散的方式找到最佳的边缘节点，通过边缘进行包转发可以有效地利用无线资源。强化学习算法用于优化最后两跳通信，以提高通信路由的适应性。该方案根据不同的上下文信息（如连接依赖性（依赖连接或独立连接）、通信类型（单播或广播）和数据包有效负载大小）为不同类型的通信选择不同的边缘节点。我们通过与各种网络条件和流量模式下的现有广播协议和单播协议进行比较，开展了广泛的仿真评估，以验证所提出方案的性能。
