Determining how to structure vehicular network environments can be done in various ways. Here, we highlight vehicle networks' evolution from vehicular ad-hoc networks (VANET) to the internet of vehicles (IoVs), listing their benefits and limitations. We also highlight the reasons in adopting wireless technologies, in particular, IEEE 802.11p and 5G vehicle-to-everything, as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments. We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems' requirements. The presentation of each paradigm is given from a historical and logical standpoint. In particular, vehicular fog computing improves on the deficiences of vehicular cloud computing, so both are not exclusive from the application point of view. We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks, showing that they complement each other and share problems and limitations. As these networks still have many opportunities to grow in both concept and application, we finally discuss concepts and technologies that we believe are beneficial. Throughout this work, we emphasize the crucial role of these concepts for the well-being of humanity.

The deployment of 5G technology has drawn attention to different computer-based scenarios. It is useful in the context of Smart Cities, the Internet of Things (IoT), and Edge Computing, among other systems. With the high number of connected vehicles, providing network security solutions for the Internet of Vehicles (IoV) is not a trivial process due to its decentralized management structure and heterogeneous characteristics (e.g., connection time, and high-frequency changes in network topology due to high mobility, among others). Machine learning (ML) algorithms have the potential to extract patterns to cover security requirements better and to detect/classify malicious behavior in a network. Based on this, in this work we propose an Intrusion Detection System (IDS) for detecting Flooding attacks in vehicular scenarios. We also simulate 5G-enabled vehicular scenarios using the Network Simulator 3 (NS-3). We generate four datasets considering different numbers of nodes, attackers, and mobility patterns extracted from Simulation of Urban MObility (SUMO). Furthermore, our conducted tests show that the proposed IDS achieved F1 scores of 1.00 and 0.98 using decision trees and random forests, respectively. This means that it was able to properly classify the Flooding attack in the 5G vehicular environment considered.

Modern Living, as we know it, has been impacted meaningfully by the Internet of Things (IoT). IoT consists of a network of things that collect data from machines (e.g., mobile devices) and people. Mobile application development is a flourishing tendency, given the increasing popularity of smartphones. Nowadays, users are accessing their desired services on the smartphone by means of dedicated applications as the latter offers a more customized and prompt service. In addition, companies are also looking to persuade users by offering interactive and effective mobile applications. Mobile application developers are using IoT to develop better applications. However, there is no generalized consensus on the selection of best architecture or even the most suitable communications protocols to be used on an IoT application development. Therefore, this article aims at presenting approaches, architectures, and methodologies relevant to the development of mobile IoT solutions.

The availability of information is a key requirement for the proper functioning of any network. When the availability problem is brought to vehicular networks, it may hinder novel vehicular services and applications and potentially put human lives at risk, as malicious users can send a massive number of spurious packets to disrupt them. Although flooding attacks in vehicular contexts have been the focus of attention of the research community, most proposed datasets are generated using simulated data and only contain the modeled network’s behavior. In this work, we generated datasets of such attacks using three realistic vehicular devices, i.e., MK5 On-board Unit (OBU). We applied a machine learning algorithm to get the first insights into the complexity of the proposed datasets, reporting the achieved Accuracy, F1-Score, Precision, and Recall.