The recent advances aiming to enable in-network service provisioning are empowering a plethora of smart infrastructure developments, including smart cities, and intelligent transportation systems. Although edge computing in conjunction with roadside units appears as a promising technology for proximate service computations, the rising demands for ubiquitous computing and ultra-low latency requirements from consumer vehicles are challenging the adoption of intelligent transportation systems. Vehicular fog computing which extends the fog computing paradigm in vehicular networks by utilizing either parked or moving vehicles for computations has the potential to further reduce the computation offloading transmission costs. Therefore, with a precise objective of reducing latency and delivering proximate service computations, we integrated vehicular fog computing with roadside edge computing and proposed a four-layer framework named FoggyEdge. The FoggyEdge framework is built at the top of named data networking and employs microservices to perform in-network computations and offloading. A real-world SUMO-based preliminary performance comparison validates FoggyEdge effectiveness. Finally, a few future research directions on incentive mechanisms, security and privacy, optimal vehicular fog location, and load-balancing are summarized.

翻译：近期旨在实现网络内服务提供的进展正在赋能众多智能基础设施的发展，包括智慧城市和智能交通系统。虽然结合路侧单元的边缘计算是邻近服务计算中颇具前景的技术，但消费级车辆对泛在计算与超低延迟日益增长的需求正在挑战智能交通系统的部署。车辆雾计算通过利用停放或行驶中的车辆进行计算，将雾计算范式延伸至车载网络，有望进一步降低计算卸载的传输成本。因此，立足于降低延迟并提供邻近服务的精确目标，我们将车辆雾计算与路侧边缘计算相结合，提出了一种名为FoggyEdge的四层框架。FoggyEdge框架构建于命名数据网络之上，并采用微服务执行网络内计算与卸载。基于真实交通场景的SUMO初步性能对比验证了FoggyEdge的有效性。最后，总结了激励机制、安全隐私、最优车辆雾位置及负载均衡等未来研究方向。