In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks.

Context-awareness is getting increasingly important for a range of mobile and pervasive applications on nowadays smartphones. Whereas human-centric contexts (e.g., indoor/ outdoor, at home/in office, driving/walking) have been extensively researched, few attempts have studied from phones' perspective (e.g., on table/sofa, in pocket/bag/hand). We refer to such immediate surroundings as micro-environment, usually several to a dozen of centimeters, around a phone. In this study, we design and implement Sherlock, a micro-environment sensing platform that automatically records sensor hints and characterizes the micro-environment of smartphones. The platform runs as a daemon process on a smartphone and provides finer-grained environment information to upper layer applications via programming interfaces. Sherlock is a unified framework covering the major cases of phone usage, placement, attitude, and interaction in practical uses with complicated user habits. As a long-term running middleware, Sherlock considers both energy consumption and user friendship. We prototype Sherlock on Android OS and systematically evaluate its performance with data collected on fifteen scenarios during three weeks. The preliminary results show that Sherlock achieves low energy cost, rapid system deployment, and competitive sensing accuracy.

Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. However, the collected human/vehicle trajectory flows are usually rather unreliable, may contain various noise and sometimes are incomplete, thus posing great challenges to existing approaches. In this paper, we present a pioneering study on robust fine-grained urban flow inference with noisy and incomplete urban flow observations, and propose a denoising diffusion model named DiffUFlow to effectively address it. Specifically, we propose an improved reverse diffusion strategy. A spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are also proposed. Then, we overlay the spatial-temporal feature map extracted by STFormer onto the coarse-grained flow map, serving as a conditional guidance for the reverse diffusion process. We further integrate the semantic features extracted by ELFetcher to cross-attention layers, enabling the comprehensive consideration of semantic information encompassing the entirety of urban data in fine-grained inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines.

The rapid proliferation of Internet of Things (IoT) devices has revolutionized various industries by enabling smart grids, smart cities, and other applications that rely on seamless connectivity and real‐time data processing. However, this growth has also introduced significant security challenges due to the scale, heterogeneity, and resource constraints of IoT systems. Traditional intrusion detection systems (IDS) often struggle to address these challenges effectively, as they require centralized data collection and processing, which raises concerns about data privacy, communication overhead, and scalability. To address these issues, this paper investigates the application of federated learning for network intrusion detection in IoT environments. We first evaluate a range of machine learning (ML) and deep learning (DL) models, finding that the random forest model achieves the highest classification accuracy. We then propose a federated learning approach that allows distributed IoT devices to collaboratively train ML models without sharing raw data, thereby preserving privacy and reducing communication costs. Experimental results using the UNSW‐NB15 dataset demonstrate that this approach achieves promising outcomes in the IoT context, with minimal performance degradation compared to centralized learning. Our findings highlight the potential of federated learning as an effective, decentralized solution for network intrusion detection in IoT environments, addressing critical challenges, such as data privacy, heterogeneity, and scalability.

Blood glucose concentration plays an important role in personal health. Hyperglycemia results in diabetes, leading to health risks such as pancreatic function failure, immunity reduce and ocular fundus diseases [6]. Meanwhile, hypoglycemia also brings complications such as confusion, shakiness, anxiety, and if not treated in time, coma or death [2]. People with diabetes need tight control of their blood glucose concentration to avoid both short-term and long-term physiological complications. In this work, we design BGMonitor, the first personalized smartphone-based non-invasive blood glucose monitoring system that detects abnormal blood glucose events by jointly tracking meal, drugs and insulin intake, physical activity and sleep quality. When BGMonitor detects an abnormal blood glucose event, it reminds the user to double-check by finger pricking or using clinical CGM devices.