Compared with traditional Internet of Vehicles (IoV) in highways or suburban areas, maintaining network connectivity and stability is an important challenge for IoV in an urban scene, which has complex road conditions. Most existing studies focus on connectivity probability and whole network connectivity analysis. They analyze end-to-end connectivity probability under a certain node distribution model, and reveal the relationship between whole network connectivity and node density. Most of their results are not applicable to IoV in an urban scene. This work presents a concept of an IoV backbone net for IoV in an urban scene, called IoVB-net for short, to realize interconnections among nodes, and formulates an accessibility model based on IoVB-net, and proposes a routing method. The experimental results demonstrate that the trend of the accessibility's theoretical value is in accordance with that of data packet delivery rate, and the proposed routing method based on accessibility can also realize higher delivery rate and lower end-to-end latency than the improved greedy traffic aware routing and greedy perimeter stateless routing.

Wireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually show high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods, lacking abilities of modeling the dynamic spatial-temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. To improve the accuracy of 5G/B5G cellular network traffic prediction, more cross-domain data was considered, a cross-service and regional fusion transfer learning strategy (Fusion-transfer) based on the spatial-temporal cross-domain neural network model (STC-N) was proposed. Multiple cross-domain datasets were integrated. The training accuracy of the target service domain based on the data characteristics of its source service domain according to the similarity between services and the similarity between different regions was improved, so the predictive performance of the model was enhanced. The experimental results show that the prediction accuracy of the traffic prediction model is significantly improved after the integration of multiple cross-domain datasets, the RMSE performance of SMS, Call and Internet service can be improved about 8.39%, 13.76% and 5.7% respectively. In addition, compared with the existing transfer strategy, the RMSE of the three services can be improved about 2.48%~13.19%.

Heterogeneous Graph Neural Networks (HGNNs) have attracted significant research attention in recent years due to their ability to capture complex interactions among various node types in heterogeneous graphs (HGs). However, existing methods face critical challenges, including the loss of graph attribute information caused by excessive emphasis on semantic information and the difficulty of effectively integrating graph attributes with semantic information. To address these issues, this paper proposes HGNN-GAMS: a Heterogeneous Graph Neural Network for Graph Attribute Mining and Semantic Fusion. The model comprises two main components: graph attribute fusion and semantic aggregation. The graph attribute fusion module captures two intrinsic features of HGs—their unique topological structures and node attributes. The semantic aggregation module, leveraging an attention mechanism, integrates diverse semantic information within HGs. Ultimately, HGNN-GAMS fuses graph attribute features and semantic features to produce the final feature representation. This work pioneers the integration of graph attributes with semantic information and validates the model’s effectiveness through extensive experiments on real-world datasets.

A group of geographically dispersed and logically collaborated emergency organizations are involved when emergency occurs. Two challenging issues are a reasonable emergency resource allocation mechanism and an efficient resource conflict checking and resolution control mechanism to ensure the conflict-free execution of global cross-organization emergency response processes. To address them, this paper proposes an approach to support emergency resource management including both intraorganization private resource management and cross-organization public resource management. The former has been fully discussed in our previous work using E-net. To cope with the latter, this paper presents a novel type of Petri net that is extended with both time and resource factors to model cross-organization emergency response processes. Then, according to the resource requirement analysis, their worst, delayed, and best cases are obtained. Next, emergency resource conflict checking and four conflict resolution strategies are proposed to resolve the detected resource conflicts for the delayed execution case. This paper shows how to use different resolution strategies to construct the conflict-free model by designing corresponding resolution controllers. Finally, their performance is evaluated by a fire emergency response example.

With the strong capability of heterogeneous graphs in accurately modeling various types of nodes and their interactions, they have gradually become a research hotspot, promoting the rapid development of the field of heterogeneous graph neural networks (HGNNs). However, most existing HGNN models rely on meta−paths for feature extraction, which can only utilize part of the data from the graph for training and learning. This not only limits the data generalization ability of deep learning models but also affects the application effect of data−driven adaptive technologies. In response to this challenge, this study proposes a new model—heterogeneous graph neural network based on regional feature extraction (HGNN−BRFE). This model enhances performance through an “extraction−fusion” strategy in three key aspects: first, it efficiently extracts features of neighboring nodes of the same type according to specific regions; second, it effectively fuses information from different regions and hierarchical neighbors using attention mechanisms; third, it specially designs a process for feature extraction and fusion targeting heterogeneous type nodes, ensuring that the rich semantic and heterogeneity information within the heterogeneous graph is retained while maintaining the node’s own characteristics during the node embedding process to prevent the loss of its own features and potential over−smoothing issues. Experimental results show that HGNN−BRFE achieves a performance improvement of 1–3% over existing methods on classification tasks across multiple real−world datasets.