Offloading in Internet of Vehicles: A Fog-Enabled Real-Time Traffic Management System

TLDR

Fog computing integrated with Internet of Vehicles supplies computational resources at the edge, ensuring low‑latency service for end users. The study proposes an offloading framework for real‑time traffic management in fog‑based IoV systems, targeting a reduction in the average response time of vehicle‑reported events. The authors build a city‑wide distributed traffic management system that uses vehicles near roadside units as fog nodes, model parked and moving fog nodes with queueing theory (moving nodes as M/M/1), and devise an approximate optimization that decomposes the problem into two subproblems to schedule traffic flows across fog nodes. Experiments on real‑world taxi‑trajectory data demonstrate that the proposed method outperforms alternatives, confirming its effectiveness.

Abstract

Fog computing has been merged with Internet of Vehicle (IoV) systems to provide computational resources for end users, by which low latency can be guaranteed. In this paper, we put forward a feasible solution that enables offloading for real-time traffic management in fog-based IoV systems, aiming to minimize the average response time for events reported by vehicles. First, we construct a distributed city-wide traffic management system, in which vehicles close to road side units can be utilized as fog nodes. Then, we model parked and moving vehicle-based fog nodes according to a queueing theory, and draw the conclusion that moving vehicle-based fog nodes can be modeled as an $M/M/1$ queue. An approximate approach is developed to solve the offloading optimization problem by decomposing it into two subproblems and scheduling traffic flows among different fog nodes. Performance analyses based on a real-world taxi-trajectory datasets are conducted to illustrate the superiority of our method.