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Reconfigurable intelligent surfaces (RIS) are expected to bring about a revolutionary transformation in vehicular networks, thus paving the way for a future characterized by connected and automated vehicles (CAV). An RIS is a planar structure comprising many passive elements that can dynamically manipulate electromagnetic waves to enhance wireless communication by reflecting, refracting, and focusing signals in a programmable manner. RIS exhibits substantial potential for improving vehicle-to-everything (V2X) communication through various means, including coverage enhancement, interference mitigation, improving signal strength, and providing additional layers of privacy and security. This article presents a comprehensive survey that explores the emerging opportunities arising from the integration of RIS into vehicular networks. To examine the convergence of RIS and V2X communications, the survey adopted a holistic approach, thus highlighting the potential benefits and challenges of this combination. In this study, we examined several applications of RIS-aided V2X communication. Subsequently, we delve into the fundamental emerging technologies that are expected to empower vehicular networks, encompassing mobile edge computing (MEC), non-orthogonal multiple access (NOMA), millimeter-wave communication (mmWave), Artificial Intelligence (AI), and visible light communication (VLC). Finally, to stimulate further research in this domain, we emphasize noteworthy research challenges and potential avenues for future exploration.

Vehicular Ad Hoc Networks (VANETs) are essential for the success of Intelligent Transportation Systems (ITS), providing real-time communication between vehicles and infrastructure. However, the highly dynamic and decentralized nature of VANETs introduces significant challenges in ensuring trust and security across the network, including security threats, communication overhead, and energy inefficiencies. This paper presents a novel blockchain-based trust management framework that addresses these issues by incorporating lightweight consensus mechanisms, optimized data propagation strategies, and energy-aware protocols. Our approach reduces communication overhead by selectively propagating trust updates, leading to a 35% decrease in overall network traffic compared to traditional broadcast-based systems. In terms of trust accuracy, our model achieves over 95% accuracy in detecting malicious nodes, significantly outperforming existing solutions. The proposed system demonstrates the identification and penalization of malicious behaviors such as Sybil attacks and false reporting with a 25% improvement in detection rate, while maintaining low latency (an average reduction of 30% compared to PoW-based systems) and efficient energy consumption, reducing energy use by up to 40%. The proposed model also incorporates a hybrid Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which further enhances its scalability and fault tolerance. Simulation results show that our framework converges to accurate trust values faster than traditional methods, ensuring that reliable trust evaluations are made in real-time, even under high mobility conditions. The combination of these optimizations ensures that our framework is not only secure but also highly efficient, capable of supporting scalable and resilient VANET deployments. Furthermore, our decentralized approach ensures that trust decisions are made in real-time without the need for a centralized authority, making the system more adaptable to the high-mobility conditions of VANETs. This research offers a comprehensive solution for VANETs trust management, significantly improving communication efficiency, trust accuracy, and energy consumption while maintaining robust security and scalability. Our proposed blockchain-based trust management system provides a secure, energy-efficient, and scalable solution for VANETs, setting the stage for future developments in secure vehicular communication networks.

Data transmission in intelligent transportation systems is being challenged by a variety of factors, such as open wireless communication channels, that pose problems related to security, anonymity, and privacy. To achieve secure data transmission, several authentication schemes are proposed by various researchers. The most predominant schemes are based on identity-based and public-key cryptography techniques. Due to limitations such as key escrow in identity-based cryptography and certificate management in public-key cryptography, certificate-less authentication schemes arrived to counter these challenges. This paper presents a comprehensive survey on the classification of various types of certificate-less authentication schemes and their features. The schemes are classified based on their type of authentication, the techniques used, the attacks they address, and their security requirements. This survey highlights the performance comparison of various authentication schemes and presents the gaps in them, thereby providing insights for the realization of intelligent transportation systems.

Towards the intelligent transportation systems, Location Based Service (LBS) are widely engaged in Vehicular Ad Hoc Networks (VANETs) that are becoming as significant application that change the human driving experience in today’s world. LBS systems facilitate the users with intelligent services by collecting an accurate location information. Due to the frequent exchange rate of the location information in an open environment, VANETs are inherently susceptible to privacy and security attacks. In past, many schemes have been proposed to ensure the privacy and security of exchanged location information; but fail to deploy in practical VANETs. At the same time, system efficiency is compromised which is another primary requirement of VANETs. Leveraging the semi-decentralized and lightweight nature of consortium blockchain technology, and Certificateless conditional privacy protection scheme to reduce the node authentication overhead, this paper introduces C onsortium B lockchain assisted C ertificateless C onditional P rivacy P rotection scheme to address the aforementioned challenges. Additionally, the proposed scheme has ability to develop anonymous regions for a particular time stamp ensuring the location privacy of vehicles. Rigorous security analysis and experiments show the practicality and resilience to various attack models, and achieve ADP 83% with maximum malicious attacks. Comparing with existing state of the art methods, the proposed scheme exhibits the privacy improvement and low computational complexity.

This paper addresses the critical challenge of tire-road contact dynamics in intelligent transportation systems, particularly for Level 4 autonomous driving. Traditional empirical models fail to accurately predict tire behavior on unstructured road surfaces, especially under low-adhesion conditions, leading to control delays and safety risks. To address these issues, we propose a novel dual-drive architecture that integrates Quantum Topological Field Theory with meta-learning techniques. A differential homeomorphism model is developed for tire contact stability, using Seiberg-Witten instanton decomposition to create a quantized representation of the contact stress field. Additionally, a multi-modal road prediction system is introduced, combining CBAM-LSTM quantum feature extraction with MAML meta-learning to generalize acceleration signals across different road conditions. Experimental validation on a hardware-in-the-loop platform demonstrates that the system reduces braking distance on ice to 32.1 meters, 38.7% shorter than traditional ABS, and achieves a slip rate control error of 1.8%. The quantum feature extraction accuracy reaches 98.5%, with a Wilson loop reconstruction error under 0.15%. This architecture overcomes key engineering challenges, providing a robust solution for L4 autonomous driving, with potential applications in tire health monitoring and intelligent road networks, enhancing safety and performance in real-world conditions.

The emerging technology that is vehicular platooning is an exciting technology. It promises to save space on congested roadways, improve safety and utilise less fuel for transporting goods, reducing greenhouse gas emissions. The technology has already been shown to be vulnerable to attack and exploitation by attackers. Attackers have several attack surfaces available for exploitation to achieve their goals (either personal or financial). The goal of this paper and its contribution to the area of research is to present the attacks and defence mechanisms for vehicular platoons and put risks of existing identified attacks forwards. Here the variety of attacks that have been identified in the literature are presented and how they compromise the wireless communications of vehicle platoons. As part of this, a risk assessment is presented to assess the risk factor of the attacks. Finally, this paper presents the range of defence and countermeasures to vehicle platooning attacks and how they protect the safe operations of vehicular platoons.

Intelligent transportation systems (ITS) play a crucial role in building sustainable and resilient urban mobility by improving traffic efficiency, reducing energy consumption, and lowering emissions. The integration of IoT technologies, particularly long-range low-power networks such as LoRaWAN, enables energy-efficient communication between vehicles and road infrastructure, supporting the sustainability goals of smart cities. However, the widespread deployment of IoT devices also introduces significant cybersecurity risks that may compromise the safety, reliability, and long-term sustainability of transportation systems. To address this challenge, we propose a method for generating synthetic network data that simulates normal traffic and DDoS attacks by randomly selecting distribution parameters for features like packets per second and unique device addresses, enabling evaluation of machine learning algorithms (e.g., Gradient Boosting, Random Forest, SVM, XGBoost) using F1-score and AUC metrics in a controlled environment. By enhancing cybersecurity and resilience in ITS, our research contributes to the development of safer, more energy-efficient, and sustainable transportation infrastructures.

Intelligent Transportation Systems (ITS) first appeared in 1868 with traffic lights. With developing technology, the need to bring a smart approach to transportation applications within the scope of speed and environmental protection has emerged. Protecting ITS infrastructure against cyber attacks has become a matter of reputation for states. It is essential to provide the necessary technological infrastructure for the integrated operation of the systems used in ITS, especially geographical location, communication, and mapping. These technological developments bring cyber attacks, risks, and many dangers that should be avoided, especially on the systems used. This study examines ITS architecture, applications, communication technologies, and new trend technologies in detail. This study includes contributing to studies in the field of ITS and preventing attacks and incidents that may occur in terms of cyber security. The most important cyber attacks that may occur in ITS applications are included. In addition, the minimum security requirements that can be taken in ITS applications and infrastructures against these attacks are included.

The development of artificial intelligence (AI) and self-driving technology is expected to enhance intelligent transportation systems (ITSs) by improving road safety and mobility, increasing traffic flow, and reducing vehicle emissions in the near future. In an ITS, each autonomous vehicle acts as a node with its own local machine learning models, which can be updated using locally collected data. However, for autonomous vehicles to learn effective models, they must be able to learn from data sources provided by other vehicles and infrastructure, utilizing innovative learning methods to adapt to various autonomous driving scenarios. Distributed learning plays a crucial role in implementing these learning tasks in an ITS. This review provides a systematic overview of distributed learning in the field of ITSs. Within an ITS, vehicles can engage in distributed learning by interacting with peers through opportunistic encounters and clustering. This study examines the challenges associated with distributed learning, focusing on issues related to privacy and security in data intelligence sharing, communication quality and speed, and trust. Through a thorough analysis of these challenges, this study presents potential research avenues to address these issues, including the utilization of incentive mechanisms that rely on reputation, the adoption of rapid convergence techniques, and the integration of opportunistic federated learning with blockchain technology.