In the process of vehicles transitioning from conventional means of transportation to mobile computing platforms, ensuring secure communication and data exchange is of paramount importance. Consequently, identity authentication has emerged as a crucial security measure. Specifically, effective authentication is required prior to the communication between the On-Board Unit (OBU) and Roadside Unit (RSU). To address vehicle identity authentication challenges in the Internet of Vehicles (VANETs), this paper proposes a three-party identity authentication and key agreement protocol based on elliptic curve public key cryptography. Considering issues such as vehicle impersonation attacks, RSU impersonation attacks, and vehicle privacy breaches in existing schemes within wireless mobile environments, this protocol introduces a trusted registry center that successfully enables mutual authentication between OBU and RSU. The proposed protocol not only enhances the VANETs system’s ability to withstand security threats but also improves the credibility and efficiency of the authentication process.

With diversified demands for location-based services (LBS), smartphone-based indoor pedestrian positioning becomes a research hotspot in the academic and industrial society. Due to the complexity of the indoor environments and the insufficient accuracy of smartphone inertial sensors, it is still challenging to get an indoor pedestrian positioning solution with stable positioning accuracy and good environmental adaptability. Aiming at this problem, a novel iBeacon deployment scheme for indoor pedestrian is proposed in this paper. Firstly, we introduce an abstract method for complex and diverse plane structures of the indoor environments. Secondly, a mapping function between positioning accuracy and pedestrian walking distance is deduced by error analysis of pedestrian dead-reckoning (PDR) method. Finally, this paper proposes a generation algorithm of iBeacon deployment scheme which not only satisfies the positioning accuracy requirement of LBSs but also greatly reduces the number of deployed iBeacons. We have carried out experimental analysis on three different plane structures of real indoor environments. And it turns out that the proposed iBeacon deployment scheme can help PDR-based indoor pedestrian positioning solutions to achieve breakthrough in indoor environment adaptability.

Objective: We propose KnvResGAT for efficient SARS-CoV-2 lineage classification by combining k-mer Natural Vector (KNV) representations with a residual multi-head Graph Attention Network (GAT) on a k-nearest-neighbor (kNN) similarity graph constructed in the KNV feature space. Results: On a time-aware per-lineage split of 182,851 curated SARS-CoV-2 genomes spanning 103 Pango lineages, KnvResGAT achieved 0.9729 accuracy and 0.9636 Macro-F1. Under the same split, it outperformed Pangolin (0.9673 accuracy, 0.9471 Macro-F1) and a strong deep baseline ResMLP (0.9654 accuracy, 0.9520 Macro-F1), demonstrating improved generalization for multi-class lineage classification.