With the rapid advancement of the Internet of Things (IoT), its applications are becoming increasingly essential in actual application. Specifically, the recent surge in electric vehicles has spurred significant advancements in vehicle ad hoc networks (VANET). Therefore, efficient data transmission is a critical challenge in IoT, especially VANET. The certificateless signcryption (CLS) scheme has high authentication efficiency, which has become increasingly important in IoT. However, most existing schemes rely on bilinear pairing, resulting in high calculation costs or failure to achieve their claimed security. Furthermore, the security of some CLS schemes is proved in the random oracle, which limits the usefulness of the CLS scheme. To further address these issues, we propose an efficient and secure CLS scheme in the standard model for VANET, which enhances its practical applicability in VANET. Our construction employs formal security proofs and cost simulations to ensure the scheme’s security and low calculation costs, making it suitable for VANET. Finally, the security and efficiency analyses prove that our scheme offers enhanced security features and superior performance compared to existing schemes.

As Vehicular Ad hoc Networks (VANETs) become integrated into daily life, drivers can conveniently transmit messages to other vehicles. However, since most messages are sent over public channels, they are vulnerable to leakage and tampering, posing risks to user privacy and security. A secure authentication scheme is essential to ensure message authenticity and integrity. Vehicles frequently cross multiple domains while driving, but existing schemes mainly support authentication within a single domain, making it difficult for vehicles from different domains to establish trusted connections. Moreover, many current approaches rely heavily on bilinear pairings, reducing efficiency and increasing communication overhead. To address these issues, we propose an efficient cross-domain authentication scheme for VANETs. Our scheme uses pseudonyms to protect vehicle privacy and allows vehicles to directly authenticate with entities from other domains without relying on a trusted authority, enabling the establishment of a secure session key. We verified the security of our protocol using ProVerif and evaluated its performance with JPBC, a Java-based cryptographic library. Results show that our approach outperforms existing methods and is well-suited for high-traffic, densely populated, and resource-constrained VANET environments.

Abstract In response to the challenges of Multi‐Object Tracking (MOT) in sports scenes, such as severe occlusions, similar appearances, drastic pose changes, and complex motion patterns, a deep‐learning framework CTGMOT (CNN‐Transformer‐GNN‐based MOT) specifically for multiple athlete tracking in sports videos that performs joint modelling of detection, appearance and motion features is proposed. Firstly, a detection network that combines Convolutional Neural Networks (CNN) and Transformers is constructed to extract both local and global features from images. The fusion of appearance and motion features is achieved through a design of parallel dual‐branch decoders. Secondly, graph models are built using Graph Neural Networks (GNN) to accurately capture the spatio‐temporal correlations between object and trajectory features from inter‐frame and intra‐frame associations. Experimental results on the public sports tracking dataset SportsMOT show that the proposed framework outperforms other state‐of‐the‐art methods for MOT in complex sport scenes. In addition, the proposed framework shows excellent generality on benchmark datasets MOT17 and MOT20.