A Network-Centric Analysis for the Internet of Vehicles and Simulation Tools

Abstract

The Internet of Vehicles (IoV) is an emerging research framework, with network and graph theories as two of the major fields. Researchers in these topics use a variety of tools and approaches to simulate and perform experimentation on their proposed methodologies. A comprehensive study to facilitate the selection of such simulation tools is lacking from the literature. In this work, we provide a systematic review of the different simulation platforms. More precisely, the contributions of this paper are fourfold: firstly, we propose a two-tier hierarchical taxonomy based on the trends in the literature; secondly, we investigate the strengths and limitations of different simulation platforms; and thirdly, we take a network theoretic approach to identify the patterns in IoV research. To this end, we create a network of the publications and populate the edges among them. Community detection is performed using Louvian and Clauset-Newman-Moore algorithms. To the best of our knowledge, this is a novel approach to reviewing the literature which provides a more in-depth analysis of the trends in the literature. Finally, we review the common datasets for IoV experimentation.

Keywords

• Complex networks
• community detection
• OSM
• SUMO
• VANET
• VEINS