The rapid advancement of the Internet of Vehicles (IoV) necessitates robust solutions that balance user privacy with efficient service delivery, particularly for personalized charging station recommendations. This paper proposes a lightweight anonymous authentication and secure computing service integration scheme based on distributed center architecture. By leveraging a distributed dual-cloud architecture, the scheme ensures secure and traceable anonymous authentication while facilitating efficient vehicle-to-service communication. Vehicle users encrypt their preference data, which is processed using matrix factorization and secure multi-party computation to deliver accurate, privacy-preserving charging station recommendations. The quick reconnection mechanism we established reduces re-authentication time costs by roughly 53 percent, while the dual-server architecture supports privacy computing for vehicles after authentication. The framework integrates multi-source heterogeneous data, such as real-time traffic and charging station status, to enhance recommendation accuracy and timeliness. Experimental results demonstrate that the proposed approach achieves strong anonymity, traceability, and low-latency performance, offering a practical and scalable solution for secure, intelligent IoV services.
This work establishes a framework for global asymptotic stability of variable-order fractional systems with short memory under an event-triggered impulsive control strategy. Through the fusion of the short-memory principle with variable-order fractional operators, the proposed approach leverages their inherent dynamic order adjustability. This enables the system to accurately characterize time-varying memory properties and respond effectively to its dynamic evolution. Sufficient criteria are deduced to guarantee global asymptotic stability while explicitly excluding Zeno behavior. Finally, a numerical simulation is provided to verify the theoretical results.
This paper introduces a hierarchical network optimization framework for UAV-assisted three-dimensional (3D) vehicular communications, addressing the limitations of traditional terrestrial networks in dynamic and obstructed environments. The proposed framework integrates terrestrial road-constrained communication with aerial enhancements, forming a multi-layer architecture that includes cellular, road, and airspace layers. Utilizing stochastic geometry, the study develops unified models for road topology and 3D UAV distribution, introducing two key innovations: (1) an equivalent node mapping mechanism to simplify 3D resource optimization while preserving height-dependent propagation characteristics, and (2) adaptive cross-layer biasing strategies to optimize user association and mitigate interference. Theoretical and experimental analyses demonstrate significant performance improvements, with cascading coverage benefits in low-to-medium signal-to-noise ratio (SNR) regimes and dominant capacity enhancements in high-SNR regions due to line-of-sight (LoS) propagation. These findings provide a robust foundation for 3D network planning in intelligent transportation systems, offering critical insights for enhancing ultra-reliable vehicular communications.
Integrated sensing and communication (ISAC) is a promising communication technique in IoT networks as it can simultaneously conduct data communications and sensing detections. In contrast to prior studies that do not consider the impact of antennas, this paper analyzes the performance of ISAC networks with directional antennas. In particular, we establish an analytical model to analyze both the sensing probability and communication probability of directional ISAC. Our model illuminates the relationship between the sensing/communication performance and key parameters such as the antenna beamwidth and node density. The accuracy of the model is validated by simulations. Based on our model, the performance of directional integrated sensing and communication networks can be improved by adjusting the antenna beamwidth and node density.
