FoggyEdge: An Information-Centric Computation Offloading and Management Framework for Edge-Based Vehicular Fog Computing

Abstract

Recent advances aiming to enable in-network service provisioning are empowering a plethora of smart infrastructure developments, including smart cities and intelligent transportation systems (ITSs). Although edge computing, in conjunction with roadside units, appears to be a promising technology for proximate service computations, the rising demands for ubiquitous computing and ultralow-latency requirements from consumer vehicles are challenging the adoption of ITSs. Vehicular fog (VF) 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 computation offloading transmission costs. Therefore, with a precise objective of reducing latency and delivering proximate service computations, we integrate VF computing with roadside edge computing and propose 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 Simulation of Urban Mobility-based preliminary performance comparison validates FoggyEdge’s effectiveness. Finally, a few future research directions on incentive mechanisms, security and privacy, optimal VF location, and load balancing are summarized.