Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review

TL;DR

This review discusses how fog computing enhances vehicular congestion detection in Internet of Vehicles systems, enabling scalable, accurate traffic management over large areas.

Contribution

It surveys connected vehicle techniques and fog computing paradigms to develop a feasible, large-scale vehicular congestion detection system for intelligent transportation.

Findings

Vehicular congestion detection is feasible with IoV technology.

Fog computing improves communication for large vehicular networks.

The system aims to meet primary traffic management goals.

Abstract

Vehicular congestion is directly impacting the efficiency of the transport sector. A wireless sensor network for vehicular clients is used in Internet of Vehicles based solutions for traffic management applications. It was found that vehicular congestion detection by using Internet of Vehicles based connected vehicles technology are practically feasible for congestion handling. It was found that by using Fog Computing based principles in the vehicular wireless sensor network, communication in the system can be improved to support larger number of nodes without impacting performance. In this paper, connected vehicles technology based vehicular congestion identification techniques are studied. Computing paradigms that can be used for the vehicular network are studied to develop a practically feasible vehicular congestion detection system that performs accurately for a large coverage area and multiple scenarios. The designed system is expected to detect congestion to meet traffic management goals that are of primary importance in intelligent transportation systems.