Commercialized 5G technology will provide reliable and efficient connectivity of motor vehicles that could support the dissemination of information under an intelligent transportation system. However, such service still suffers from risks or threats due to malicious content producers. The traditional public key infrastructure (PKI) cannot restrain such untrusted but legitimate publishers. Therefore, a trust-based service management mechanism is required to secure information dissemination. The issue of how to achieve a trust management model becomes a key problem in the situation. This paper proposes a novel prototype of the decentralized trust management system (DTMS) based on blockchain technologies. Compared with the conventional and centralized trust management system, DTMS adopts a decentralized consensus-based trust evaluation model and a blockchain-based trust storage system, which provide a transparent evaluation procedure and irreversible storage of trust credits. Moreover, the proposed trust model improves blockchain efficiency by only allowing trusted nodes participating in the validation and consensus process. Additionally, the designed system creatively applies a trusted execution environment (TEE) to secure the trust evaluation process together with an incentive model that is used to stimulate more participation and penalize malicious behaviours. Finally, to evaluate our new design prototype, both numerical analysis and practical experiments are implemented for performance evaluation.

The mobile industry's evolution from 4G to 5G will lead to a deep progress on mobile applications that are widely used in some new environments, such as vehicular social networks (VSNs). In VSNs, which are considered the first automobile social networks, vehicular communication can facilitate large-scale data sharing between drivers and their neighbours. However, malicious users of VSNs can also disseminate false information over the network. Traditional public key infrastructure (PKI) cannot recognize these malicious users, as they all have authorized identities. Thus, a trust management mechanism is introduced to secure vehicular social data. This paper demonstrates a high-level trust management model and its deployment scheme based on a vehicular cloud system. We propose a layered trust management mechanism that benefits from efficient use of physical resources (e.g., computing, storage, communication cost) and explore its deployment in a VSN scenario based on a three-layer cloud computing architecture. Moreover, performance modeling of the proposed trust management scheme is conducted through a novel formal compositional approach - Performance Evaluation Process Algebra (PEPA). PEPA has superior features in compositionality and parsimony, which means that it can efficiently model systems with layered architectures and complex behaviours. PEPA also supports various numerical analyses through calculating its underlying continuous time Markov chains (CTMCs) directly or solving a set of approximated ordinary differential equations (ODEs). According to analysis outcomes, we analyzed several key performance properties of the scheme and related capacity issues in deployment. The findings also reveal an efficient investigation approach for evaluating the performances of trust models.

Vehicular networks enable an efficient communication with the aim of improving data dissemination among vehicles. However, a growing number of vehicles expect to conduct data dissemination through road side facilities which cause an increment of network load. To address the issue, this letter applies fog computing technologies for enhancing vehicular network that is planned as a layered network architecture. Moreover, two dynamic scheduling algorithms are proposed on the fog computing scheme for the data scheduling in vehicular networks. These algorithms can dynamically adapt a changeable network environment and benefit in efficiency. For performance analysis, a compositional formal method, named performance evaluation process algebra, is applied to model scheduling algorithm in a fog-based vehicular network.

The integration of computer vision (CV) and generative artificial intelligence (GenAI) into smart agriculture has revolutionised traditional farming practices by enabling real-time monitoring, automation, and data-driven decision-making. This review systematically examines the applications of CV in key agricultural domains, such as crop health monitoring, precision farming, harvesting automation, and livestock management, while highlighting the transformative role of GenAI in addressing data scarcity and enhancing model robustness. Advanced techniques, including convolutional neural networks (CNNs), YOLO variants, and transformer-based architectures, are analysed for their effectiveness in tasks like pest detection, fruit maturity classification, and field management. The survey reveals that generative models, such as generative adversarial networks (GANs) and diffusion models, significantly improve dataset diversity and model generalisation, particularly in low-resource scenarios. However, challenges persist, including environmental variability, edge deployment limitations, and the need for interpretable systems. Emerging trends, such as vision–language models and federated learning, offer promising avenues for future research. The study concludes that the synergy of CV and GenAI holds immense potential for advancing smart agriculture, though scalable, adaptive, and trustworthy solutions remain critical for widespread adoption. This comprehensive analysis provides valuable insights for researchers and practitioners aiming to harness AI-driven innovations in agricultural ecosystems.

The deep integration of artificial intelligence (AI) and Internet of Things (IoT) technologies has promoted the application of Artificial Intelligence of Things (AIoT) in smart agriculture, driving substantial transformations in agricultural data management systems and facilitating sustainable agricultural development. This paper reviews the key AIoT data management technologies employed in smart agriculture, systematically exploring the technical applications and practical implementations across four architectural layers. These layers (i.e. perception layer, transport layer, data platform layer, and application layer) correspond to agricultural information sensing, data transmission, storage processing, and intelligent decision-making, respectively. Research indicates that, through the deployment of diverse sensing devices, low-latency short-range communications, wide-area wireless networks, cloud-edge collaborative computing, blockchain-based data storage, and AI-driven agricultural decision support systems, the data management framework of smart agriculture is undergoing continuous and intelligent enhancement. AIoT exhibits significant potential in enhancing agricultural productivity, optimizing resource allocation, and improving environmental adaptability. However, significant challenges remain, particularly in protecting agricultural data privacy and achieving real-time interoperability across heterogeneous platforms. The paper concludes by outlining future research directions, offering valuable insights for researchers in the field of smart agriculture.