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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.

The Internet of Vehicles (IoV) aims to establish a network of autonomous vehicles that exchange messages with one another and the infrastructure such as road-side units and a central trust authority. These messages must be secure in transmission to ensure network security and utilize as little resources as possible. Currently, there is much research on the network design and security in the standards for Dedicated Short Range Communication (DSRC) and 5G mobile networks, but few studies have analyzed resource utilization and optimization. The proposed method applies Prim’s algorithm to a thorough security scheme, Privacy-Preserving Dual Authentication Scheme (PPDAS), to decrease the power consumption. Broadcast-based networks such as DSRC and 5G suffer from a poor power consumption, in a quadratic growth while applying Prim’s achieves a linear growth. Experimentation is performed on multiple security schemes to justify the selection of PPDAS and the overhead applied. The results demonstrate a decrease by a magnitude of 44.57 in power usage for message dissemination. Such results pave the path for a scalable IoV.

The Internet of Vehicles (IoV) aims to establish a network of autonomous and connected vehicles that communicate with one another and with road-side units (RSUs) and a central trust authority (TA). Messages must be efficiently and securely disseminated to conserve resources and preserve network security. Currently, there is much research on the security schemes and methods of disseminating messages. Furthermore, there is a deficiency of information regarding resource optimization. This paper takes an interdisciplinary approach by merging both cybersecurity and data science to optimize and secure the network. The proposed method applies Prim's algorithm to an existing vehicular security scheme, Privacy-Preserving Dual Authentication Scheme (PPDAS), to further network efficiency in terms of time consumption. When a dual authentication security scheme is in place, the time taken for message dissemination follows a quadratic growth; applying Prim's algorithm to the security scheme reduces the time to disseminate messages to a linear growth. The number of messages sent was decreased by a magnitude of up to 44.57. Contemporary security schemes are compared with PPDAS to justify the overhead consumption. The usage of network resources, such as time, is reduced, which substantially enhances the performance of the vehicular network and allows for the scalability of the IoV.

With the ever-growing generation and collection of data, there are ample opportunities to extract useful information from big data. The transportation industry, particularly the taxi companies, are a significant contributor to this data age. This research analyzes a 2016 voluminous taxi dataset from the City of Chicago to find impactful transportation trends for determining city hotspots based on time and location. Customer satisfaction was used as a way of deciding which taxi companies need to look at improving their customer service. Linear regression models were used to estimate tips relative to the distance traveled and the time taken. The haversine distance was utilized to pair the latitude and longitude coordinates of drop-offs and their next pickup. To maximize the driver's earnings, information on tips, and to analyze the average range to drivers next fare were combined. Stakeholders, customers, and transportation authorities can use the results of this analysis to plan better commute patterns.