Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

https://ir.nctu.edu.tw:443/bitstream/11536/145531/1/3ecec2a2f6e38b0824ccca63f66259e9.pdf

Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

Autonomous vehicles (AVs) attract a lot of attention recently. They are expected to assist/replace the human drivers in maneuvering the vehicle, thereby reducing the likelihood of road accidents caused by human error, as a means to improve the road traffic safety. However, AVs have their inherent safety and security challenges, which have to be addressed before they are ready for wide adoption. This paper presents an overview of recent research on AV safety failures and security attacks, as well as the available safety and security countermeasures.

A review on safety failures, security attacks, and available countermeasures for autonomous vehicles

Recently, connected vehicles (CV) are becoming a promising research area leading to the concept of CV as a Service (CVaaS). With the increase of connected vehicles and an exponential growth in the field of online cab booking services, new requirements such as secure, seamless and robust information exchange among vehicles of vehicular networks are emerging. In this context, the original concept of vehicular networks is being transformed into a new concept known as connected and autonomous vehicles. Autonomous vehicular use yields a better experience and helps in reducing congestion by allowing current information to be obtained by the vehicles instantly. However, malicious users in the internet of vehicles may mislead the whole communication where intruders may compromise smart devices with the purpose of executing a malicious ploy. In order to prevent these issues, a blockchain technique is considered the best technique that provides secrecy and protection to the control system in real time conditions. In this paper, the issue of security in smart sensors of connected vehicles that can be compromised by expert intruders is addressed by proposing a blockchain framework. This study has further identified and validated the proposed mechanism based on various security criteria, such as fake requests of the user, compromise of smart devices, probabilistic authentication scenarios and alteration in stored user’s ratings. The results have been analyzed against some existing approach and validated with improved simulated results that offer 79% success rate over the above-mentioned issues.

A Blockchain Framework for Securing Connected and Autonomous Vehicles.

The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) communication for intelligent transportation systems. There is an urgent need for distributed machine learning techniques that can take advantages of massive interconnected networks with explosive amount of heterogeneous data generated at the network edge. In this study, a two-layer federated learning model is proposed to take advantages of the distributed end-edge-cloud architecture typical in 6G environment, and to achieve a more efficient and more accurate learning while ensuring data privacy protection and reducing communication overheads. A novel multi-layer heterogeneous model selection and aggregation scheme is designed as a part of the federated learning process to better utilize the local and global contexts of individual vehicles and road side units (RSUs) in 6G supported vehicular networks. This context-aware distributed learning mechanism is then developed and applied to address intelligent object detection, which is one of the most critical challenges in modern intelligent transportation systems with autonomous vehicles. Evaluation results showed that the proposed method, which demonstrates a higher learning accuracy with better precision, recall and F1 score, outperforms other state-of-the-art methods under 6G network configuration by achieving faster convergence, and scales better with larger numbers of RSUs involved in the learning process.

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Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

Vehicular ad-hoc networks (VANETs) play an important role in intelligent transportation systems for improving security and efficiency. However, due to dynamic characteristics of the vehicular environment, routing remains a significant challenge in the VANETs. While single-layer routing protocols based on the traditional layered open systems interconnection (OSI) model are readily available, they often do not make use of important parameters at the lower three layers of the OSI model when making routing decision. Hence, for making optimal routing decision to gain superior network performance, there is a need to design cross-layer routing that allows information exchange between layers. In this article, a survey of the existing single-layer and cross-layer routing techniques in VANETs is presented, emphasizing on cross-layer routing protocols that utilize information at the physical, medium access control and network layers as routing parameters. An overview and challenges of routing are given, followed by a brief discussion of single-layer routing with more focus on geographic routing. Cross-layer routing protocols are then discussed in detail. The article then elaborates on some advantages and disadvantages of the existing routing approaches, cross-layer routing parameter selection and cross-layer design issues. Finally, some open research challenges in developing efficient routing protocols in the VANETs are highlighted.

Routing in Vehicular Ad-hoc Networks: A Survey on Single- and Cross-Layer Design Techniques, and Perspectives

Vehicular ad hoc networks (VANETs) have been quite a hot research area in the last few years. Due to their unique characteristics such as high dynamic topology and predictable mobility, VANETs attract so much attention of both academia and industry. In this paper, we provide an overview of the main aspects of VANETs from a research perspective. This paper starts with the basic architecture of networks, then discusses three popular research issues and general research methods, and ends up with the analysis on challenges and future trends of VANETs.

https://journals.sagepub.com/doi/pdf/10.1155/2015/745303

Hossein Pishro-Nik

Papers

Vehicular ad hoc networks: architectures, research issues, methodologies, challenges, and trends