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

TL;DR

FoggyEdge is a four-layer framework that combines vehicular fog computing with roadside edge computing, utilizing named data networking and microservices to reduce latency and improve in-network service provisioning for intelligent transportation systems.

Contribution

The paper introduces FoggyEdge, a novel framework integrating vehicular fog and roadside edge computing using named data networking and microservices for efficient, low-latency service delivery.

Findings

Preliminary performance comparison shows FoggyEdge's effectiveness.

Framework reduces latency in vehicular service provisioning.

Validates potential of vehicular fog computing in real-world scenarios.

Abstract

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.