  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.
