The Internet of Autonomous Vehicles (IoAV) faces growing challenges in user privacy and communication security, stemming from dynamic network topologies induced by highspeed vehicle mobility, resource-constrained onboard devices, and the inherent tension between identity anonymity and traceability in latency-critical applications. Given the distributed architecture of fog computing and the limited storage and computational capabilities of vehicles, conventional anonymous authentication and centralized key negotiation mechanisms prove insufficient in addressing these issues. In response, We propose a distributed authentication and key negotiation protocol that combines multifactor biometrics, zero-knowledge proof (ZKP), and physical unclonable function (PUF) without relying on a trusted third party. Specifically, we design an efficient ZKP algorithm based on Chebyshev polynomials with low overhead and strong anonymity. Our key innovation is the implementation of independent key negotiation of three untrusted entities in a single protocol cycle, enabling 23 security features and functions. The performance analysis shows that the scheme takes only 17 ms to complete the protocol flow, and it reduces vehicle memory usage by 33% to 83%, service latency by 61% to 83%, and communication overhead by 12% to 50% compared to existing schemes.

Although vehicular ad hoc networks (VANETs) significantly enhance traffic convenience, the propagation of erroneous information by malicious vehicles remains a challenging issue. To maintain message reliability, it is crucial to establish a trust management model that can promptly detect malicious vehicles and identify false messages. This article presents a novel trust management model based on blockchain, machine learning, and active detection technology. In the proposed model, we designed a trust evaluation scheme to evaluate the credibility by calculating the direct and indirect trust of the vehicle. To achieve this goal, we use active detection technology to detect indirect trust in vehicles, and then store it in the blockchain. The direct trust of the vehicle is calculated using a Bayesian classifier. The use of active detection technology speeds up the process of filtering out malicious vehicles. Machine learning technology simplifies the complex iterations involved in computing the trust value. Finally, the use of blockchain ensures the consistency and tamper-proofing of the trusted data. The simulation outcomes demonstrate that our approach outperforms the present trust management models.

This paper presents a variable density (sinusoidal) antenna deployment scheme which is designed for mobile relay (MR) system of the high-speed train. By analyzing the large-scale fading under the high-speed railway (HSR) wireless channel environment, the instantaneous channel capacity and the total service amount of several antenna deployments are derived. Theoretical analysis and simulation results indicate that the proposed deployment can provide higher capacity for the HSR relay system. Comparing with several traditional deployments, the proposed deployment utilizes the feature of the HSR wireless environment, provides better coverage to the edge of the base stations (BS). In addition, an antenna selection scheme is proposed based on the sinusoidal deployment.