Vehicular Ad hoc Networks (VANET) broadcast messages regarding road and environmental conditions. Due to its design, VANET inadvertently introduced security and privacy issues. Many researchers have suggested various approaches to address these shortcomings as the deployment of VANET becomes more widespread. Nevertheless, these solutions could not address all the security and privacy shortcomings in VANET. Furthermore, the proposed approaches incur high costs in terms of computation due to the complexity involved in doing so sequentially. One of the significant approaches used in VANET security and privacy mitigation is identity-based schemes. This paper provides a comprehensive survey on VANETs and the entities involved, attack models, and an analysis of the security and privacy requirements for identity-based security and privacy schemes for VANETs.

Vehicular fog systems that are enabled by fifth-generation (5G) networks can significantly improve traffic safety and efficiency through vehicle-to-vehicle communication. Vehicles must have their legal status verified before they can join the network, as vehicular systems depend on open communication. Consequently, several anonymous authentication schemes have been created to withstand malicious vehicles, with the foundations being bilinear pairing or elliptic curve cryptography. But quantum attacks can easily defeat these schemes. To address this problem, this study introduces a 5G-supported vehicular fog system anonymous authentication strategy that makes use of lattice-based cryptography. The suggested system communicates with trusted authorities (TAs) and automobiles over the 5G-base station’s communication range, and it employs fog servers to authenticate vehicles and distribute anonymous data. By recognizing the cars and the users they are affiliated with, the TA in the suggested method can keep tabs on everyone working on a specific task. With the use of ProVerif, a protocol verification tool, we were able to determine that our authentication mechanism effectively hides user, vehicle, and fog server data. After calculating the associated computation and transmission costs, we integrated the suggested technique with state-of-the-art anonymous authentication utilizing lattice-based encryption to ward off quantum attacks. The computational costs of the proposal technique for message signing and batch/single verification are 0.3149 ms, 0.0724 ms, and 0.0724 n ms, respectively. Whereas, 18448 bits is the communication cost of the suggested method. Thus, the results show that our solution has a reduced processing cost for creating and verifying message signatures, but a much higher transmission cost.

In today’s world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in 5th-generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs), such as user experience, quality of service (QoS), or other important aspects of the network. Advanced wireless technologies have been integrated into 5G network designs at multiple network layers to address this difficulty. The integration of emerging technology trends, such as machine learning (ML), which is a subset of artificial intelligence (AI), and AI’s rapid improvements have made the integration of these trends into 5G networks a significant topic of research. The primary objective of this survey is to analyze AI’s integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

Vehicular fog computing (VFC) is a developing concept that utilizes the ideas of fog computing to facilitate immediate communication and cooperative decision-making among vehicles. However, guaranteeing safe authentication in VFC presents notable difficulties as a result of characteristics such as dynamic network topology, extensive mobility, and limitations on resources. This paper introduces D-BlockAuth, an innovative authentication mechanism for 5G-assisted VFC that utilizes a dual blockchain approach. To reach the most vehicles, this link makes use of all the features of the fifth-generation base station (5G-BS). D-BlockAuth employs two blockchains: a permissioned blockchain to handle long-term identities and a consortium blockchain to enable streamlined and effective authentication at the fog layer. The D-BlockAuth concept incorporates advanced cryptographic techniques such as ring signatures and group signatures, which provide improved privacy and anonymity for vehicles within the network. The study provides a comprehensive description of the architecture of D-BlockAuth, examines its security characteristics, and assesses its performance using simulations. The results indicate that D-BlockAuth successfully performs both efficient and safe authentication in VFC, while also maintaining user privacy.

International audienceIn the next generation wireless networks, the growing demand for new wireless applications is accompanied with high expectations for better quality of service (QoS) fulfillment especially for multimedia applications. Furthermore, the coexistence of future unlicensed users with existing licensed users is becoming a challenging task in next generation communication systems to overcome the underutilization of the spectrum. A QoS and interference aware resource allocation is thus of special interest in order to respond to the heterogeneous constraints of the next generation networks. In this work, we address the issue of resource allocation under heterogeneous constraints for unlicensed multi-band ultra-wideband (UWB) systems in the context of Future Home Networks, i.e. WPAN. The problem is first studied analytically using a heterogeneous constrained optimization problem formulation. After studying the characteristics of the optimal solution, we propose a low-complexity suboptimal algorithm based on a cross-layer approach that combines information provided by the PHY and MAC layers. While the PHY layer is responsible for providing the channel quality of the unlicensed UWB users as well as their interference power that they cause on licensed users, the MAC layer is responsible for classifying the unlicensed users using a two-class based approach that guarantees for multimedia services a high-priority level compared to other services. Combined in an efficient and simple way, the PHY and MAC information present the key elements of the aimed resource allocation. Simulation results demonstrate that the proposed scheme provides a good tradeoff between the QoS satisfaction of the unlicensed applications with hard QoS requirements and the limitation of the interference affecting the licensed users