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.
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