Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been proposed to obtain and recommend reliable platoon head vehicles. These trustworthiness evaluation protocols for PSRS fail to achieve both reliability and fairness. We first provide a reliable trustworthiness evaluation method to ensure that the reputation level of platoon head vehicle can be calculated by cloud service provider (CSP) with the help of key agreement mechanism and truth discovery technology. The semi-trusted entity CSP may maliciously tamper with the reputation level of the platoon head vehicle. We also provide a reputation level confirmation method to ensure the fairness of trustworthiness evaluation. Formal security proof and security analysis are provided to show that our trustworthiness evaluation protocol can achieve the goals of privacy protection, reliability, fairness and resistance to several security attacks. Experiments demonstrate that this protocol can save execution time and achieve reliable and fair trustworthiness evaluation for PSRS.

Real-time performance is of utmost significance for communication in certain specific scenarios of vehicle-to-infrastructure (V2I) like collision warning systems. Vehicle-to-Everything (V2X) Broadcast signcryption is very suitable for these scenarios. However, current solutions prioritize generality, but may not be suitable for specialized communication situations, and many broadcast signcryption schemes suffer from low communication verification efficiency due to the sequence of decryption before verification. Moreover, most of existing broadcast signcryption schemes with single cryptosystem are not applicable for the heterogeneous networks of different Internet of Vehicles. To address these challenges, an efficient conditional privacy-preserving heterogeneous broadcast signcryption scheme(ECPHBS) is proposed, improving roadside unit verification to support batch verification of ciphertexts through a pre-authentication mechanism, and allowing vehicles to conduct secure communication with roadside units under the certificateless cryptosystem and the identity-based cryptosystem. Meanwhile, a tracking and revocation mechanism was introduced to achieve conditional privacy protection. Our formal security analysis demonstrates that the ECPHBS scheme formally achieves IND-CCA2 security under the CDH assumption and EUF-CMA security under the ECDL problem. Experimental results confirm its superior verification efficiency, especially with an increasing number of receiving RSUs, and a constant communication overhead. Furthermore, the RSU service capability analysis shows that our scheme enables RSUs to fully handle communication requests from approximately 500 vehicles within a 150-meter range, outperforming comparative schemes.

Cross-modal retrieval is crucial for achieving accurate and efficient information retrieval by establishing semantic correlations between heterogeneous images and text. However, traditional image-text training sets suffer from information asymmetry, which includes short lengths and limited sentence structures. This phenomenon often results in insufficient representations of essential visual information. We introduce Rich-Dataset, which offers extensive semantic information. It includes diverse real-life image-text pairs and AI-generated content across domains such as news, entertainment, education, and posters. Compared with classic benchmarks such as Flickr30k and MS-COCO, RichDataset exhibits a novel and balanced distribution. Existing cross-modal retrieval models face challenges in extracting distinct features from the emerging data, leading to low retrieval accuracy. We propose SGG-MVAR, a comprehensive retrieval model guided by multiview scene information and semantic relationships. Leveraging a scene knowledge database, our model parses scene graphs and identifies differences in attributes and relationships. We conduct extensive experiments to evaluate our proposed dataset and model. All experimental results consistently demonstrate a significant improvement in recall for cross-modal retrieval.