Particularly as environmental issues and data privacy become more important, the fast growth of supply chain systems calls for ever more complex demand forecasting methods. Often, conventional techniques find it difficult to compromise these conflicting needs, which results in poor performance in either data security, environmental effect, or prediction accuracy. Artificial intelligence Internet of Things-enabled federated demand forecasting for green supply chains with a privacy-preserving carbon reference effect mechanism (AFED-Green/PCREM) is presented in this study. This new framework combining artificial intelligence and the Internet of Things (AIoT) features with federated learning provides sustainable supply chain demand forecasting. Using a distributed network of AIoT sensors, our method allows merchants to train demand prediction models cooperatively while preserving data privacy and, including environmental consciousness. The system uses a carbon reference effect mechanism that tracks how past emission levels affect consumer behavior and demand trends. Simulation findings show better performance than state-of-the-art baselines, with a 27% increase in prediction accuracy, a 42% drop in carbon emissions, and information leakage below 0.05%.

With the rapid development of Internet of Vehicles technology, communication and data exchange between vehicles have become an important part of modern traffic management. A content placement and edge collaborative caching solution based on deep reinforcement learning is proposed in this paper, aiming to address the data processing and storage challenges faced by Internet of Vehicles systems. Utilizing the collaborative caching between smart vehicles and roadside units employs deep reinforcement learning methods to find and design a collaborative caching solution for the Internet of Vehicles edge. It uses content segmentation technology to divide and cache content fragments in advance to reduce the central server load and network pressure, thereby adapting to the randomness of vehicle mobility and communication duration. The experimental results show that the proposed scheme can effectively reduce the load on the central server, reduce network latency, and improve cache hit rate, providing a flexible and efficient solution for real-time communication and data exchange in the Internet of Vehicles system.

In recent years, with the advancement of wireless communication and location acquisition technology in the context of modern smart cities, and the increasing popularity of mobile devices with location capabilities based on Social Internet of Things, we can now easily introduce location services into traditional social networks. How to effectively use these massive data to provide decision support for smart cities is an emerging task. It is necessary to find an efficient way to effectively extract and represent useful information from location-based social networks (LBSNs) by dealing with the data heterogeneity. Against this background, this article proposes a heterogeneous graph embedding framework for LBSN analysis named location based social network embedding (LBSNE). LBSNE framework first constructs the heterogeneous neighborhood of a node by formalizing a metapath-based random walk on LBSNs. Then, it leverages the learned heterogeneous neighborhood sequence to do network embedding by employing a heterogeneous skip-gram model. The effectiveness of the proposed model is evaluated on the tasks of location recommendation and visitor predict on two LBSN datasets.

Safe and scalable dynamic autonomous data interaction between medical institutions can increase the number of clinical trial records, which is of great significance for improving the level of medical trial collaboration, especially for clinical decision-making with regard to rare diseases. Through a preset authorization access and consensus mechanism, consortium chain provides integrity and traceability management for medical clinical data. However, how to enable users have ownership of their own medical data and share their medical data safely and dynamically between different medical institutions remains an area of particular concern. To achieve dynamic communication between medical consortium chains, this paper proposes (i) a cross-chain communication mechanism by simplifying the heterogeneous node communication topology and (ii) the construction rules of the node identity credibility path-proof to carry out dynamic construction and verification of the path-proof for cross-chain transactions. In addition, the consensus of the cross-chain transaction is modeled as a threshold digital signature process with multiple privileged subgroups; thus, the intra-chain consortium consensus based on the verification node list is extended to the cross-chain consensus. A smart contract deployment and execution scheme based on rational node value transfer mechanism is proposed by analyzing the value transfer game between nodes. Experimental results showed that the proposed scheme can not only enable patients to share their records safely and autonomously in an authorized medical consortium chain within milliseconds but also realize dynamic adaptive interaction among heterogeneous consortium chains.