Accurate classification of sleep stages based on bio-signals is fundamental not only for automatic sleep stage annotation, but also for clinical health management and continuous sleep monitoring. Traditionally, this task relies on experienced clinicians to manually annotate data, a process that is both time-consuming and labor-intensive. In recent years, deep learning methods have shown promise in automating this task. However, three major challenges remain: (1) deep learning models typically require large-scale labeled datasets, making them less effective in real-world settings where annotated data is limited; (2) significant inter-individual variability in bio-signals often results in inconsistent model performance when applied to new subjects, limiting generalization; and (3) existing approaches often overlook the high-order relationships among bio-signals, failing to simultaneously capture signal heterogeneity and spatial-temporal dependencies. To address these issues, we propose MetaSTH-Sleep, a few-shot sleep stage classification framework based on spatial-temporal hypergraph enhanced meta-learning. Our approach enables rapid adaptation to new subjects using only a few labeled samples, while the hypergraph structure effectively models complex spatial interconnections and temporal dynamics simultaneously in EEG signals. Experimental results demonstrate that MetaSTH-Sleep achieves substantial performance improvements across diverse subjects, offering valuable insights to support clinicians in sleep stage annotation.

Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems, enabling numerous edge devices to collaboratively train artificial intelligence (AI) models while maintaining data privacy. To overcome the communication bottlenecks associated with centralized parameter servers, decentralized federated learning (DFL), which leverages peer-to-peer (P2P) communication, has been extensively explored in the research community. Although researchers design a variety of DFL approaches to ensure model convergence, its iterative learning process inevitably incurs considerable cost along with the growth of model complexity and the number of participants. These costs are largely influenced by the dynamic changes in topology in each training round, particularly its sparsity and connectivity conditions. Furthermore, the inherent resources heterogeneity in the edge environments affects energy efficiency of the learning process, while data heterogeneity degrades model performance. These factors pose significant challenges to the design of an effective DFL framework for EC systems. To this end, we propose Hat-DFed, a heterogeneity-aware and cost-effective decentralized federated learning framework. In Hat-DFed, the topology construction is formulated as a dual optimization problem, which is then proven to be NP-hard, with the goal of maximizing model performance while minimizing cumulative energy consumption in complex edge environments. To solve this problem, we design a two-phase algorithm that dynamically constructs optimal communication topologies while unbiasedly estimating their impact on both model performance and energy cost. Additionally, the algorithm incorporates an importance-aware model aggregation mechanism to mitigate performance degradation caused by data heterogeneity. Extensive experiments demonstrate that Hat-DFed outperforms state-of-the-art baselines, achieving an average 1.8% improvement in test accuracy while reducing total energy cost by 36.9% throughout the learning process.

Recent advancements in Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) have demonstrated notable promise in the realm of intelligent traffic signal control, facilitating the coordination across multiple intersections. However, the traditional methods rely on standard graph structures often fail to capture the intricate higher-order spatio-temporal correlations inherent in real-world traffic dynamics. Standard graphs cannot fully represent the spatial relationships within road networks, which limits the effectiveness of graph-based approaches. In contrast, directed hypergraphs provide more accurate representation of spatial information to model complex directed relationships among multiple nodes. In this paper, we propose DHLight, a novel multi-agent policy-based framework that synergistically integrates directed hypergraph learning module. This framework introduces a novel dynamic directed hypergraph construction mechanism, which captures complex and evolving spatio-temporal relationships among intersections in road networks. By leveraging the directed hypergraph relational structure, DHLight empowers agents to achieve adaptive decision-making in traffic signal control. The effectiveness of DHLight is validated against state-of-the-art baselines through extensive experiments in various network datasets. We release the code to support the reproducibility of this work at https://github.com/LuckyVoasem/Traffic-Light-control

With the proliferation of data-driven services, the volume of data that needs to be processed by satellite networks has significantly increased. Federated learning (FL) is well-suited for big data processing in distributed and resource-constrained satellite environments. However, achieving robust convergence while minimizing processing time and energy consumption remains challenging. To this end, we propose a hierarchical clustered federated learning framework, FedHC. This framework employs a combined feature based dynamic clustering algorithm at the cluster aggregation stage, grouping satellites into different clusters and designating a cluster center as the parameter server (PS) to accelerate model aggregation. Several communicable cluster PS satellites are then selected through ground stations to aggregate global parameters, facilitating the FL process. Moreover, a meta-learning-driven satellite re-clustering algorithm is introduced to enhance adaptability to dynamic satellite cluster changes. Extensive experiments conducted on a satellite network testbed demonstrate that FedHC can significantly reduce processing time (up to 3x) and energy consumption (up to 2x) compared to other comparative methods while maintaining model accuracy.

Hypergraphs are increasingly utilized in both unimodal and multimodal data scenarios due to their superior ability to model and extract higher-order relationships among nodes, compared to traditional graphs. However, current hypergraph models are encountering challenges related to imbalanced data, as this imbalance can lead to biases in the model towards the more prevalent classes. While the existing techniques, such as GraphSMOTE, have improved classification accuracy for minority samples in graph data, they still fall short when addressing the unique structure of hypergraphs. Inspired by SMOTE concept, we propose HyperSMOTE as a solution to alleviate the class imbalance issue in hypergraph learning. This method involves a two-step process: initially synthesizing minority class nodes, followed by the nodes integration into the original hypergraph. We synthesize new nodes based on samples from minority classes and their neighbors. At the same time, in order to solve the problem on integrating the new node into the hypergraph, we train a decoder based on the original hypergraph incidence matrix to adaptively associate the augmented node to hyperedges. We conduct extensive evaluation on multiple single-modality datasets, such as Cora, Cora-CA and Citeseer, as well as multimodal conversation dataset MELD to verify the effectiveness of HyperSMOTE, showing an average performance gain of 3.38% and 2.97% on accuracy, respectively.

In satellite computing applications, such as remote sensing, tasks often involve similar or identical input data, leading to the same processing results. Computation reuse is an emerging paradigm that leverages the execution results of previous tasks to enhance the utilization of computational resources. While this paradigm has been extensively studied in terrestrial networks with abundant computing and caching resources, such as named data networking (NDN), it is essential to develop a framework appropriate for resource-constrained satellite networks, which are expected to have longer task completion time. In this paper, we propose CCRSat, a collaborative computation reuse frame-work for satellite edge computing networks. CCRSat initially implements local computation reuse on an independent satellite, utilizing a satellite reuse status (SRS) to assess the efficiency of computation reuse. Additionally, an inter-satellite computation reuse algorithm is introduced, which utilizes the collaborative sharing of similarity in previously processed data among multiple satellites. The evaluation results tested on real-world datasets demonstrate that, compared to comparative scenarios, our proposed CCRSat can significantly reduce task completion time by up to 62.1% and computational resource consumption by up to 28.8%.

Ensuring seamless connectivity is crucial in Vehicular Networks within Intelligent Transportation Systems (ITS). These networks facilitate real-time communication between vehicles and infrastructure, such as roadside units (RSUs) and base stations, enabling applications like traffic management, collision avoidance, and infotainment services. However, maintaining stable connections remains challenging due to network complexity, variable vehicle speeds, and frequent topology changes. These factors lead to increased latency, energy consumption, unnecessary handovers (ping-pong effect), and packet loss, particularly in low cellular coverage areas. To address these challenges, a heterogeneous approach integrating cellular networks (5G, LTE) and Dedicated Short-Range Communication (DSRC) is essential for robust connectivity and high-speed data transfer. Intelligent Vertical Handover (VHO) algorithms are necessary to ensure seamless connections to edge servers and optimize network performance in dynamic vehicular environments. This study proposes a novel handover management approach by integrating K-means clustering, a Deep Maxout Network (DMN), and the Dung Beetle Optimizer (DBO). K-means clustering identifies potential handover margin areas, the DMN evaluates handover necessity based on Received Signal Strength (RSS), vehicle speed, and latency, while the DBO assigns vehicles to servers by considering latency and energy consumption. Further analysis of server assignments mitigates the ping-pong effect by comparing current and previous selections. Simulation results in a low-coverage vehicular scenario show that the proposed method improves decision delay, latency, energy consumption, handover failure rate, ping-pong effect, and throughput. The findings demonstrate extended connection durations, lower latency, decreased energy consumption, and minimal packet loss, contributing to enhanced network efficiency and reliability in vehicular management.

Federated learning (FL) facilitates model training collaboration without needing to compromise on data privacy, which is beneficial for Internet of Things (IoT) settings. However, deploying FL on resource-constrained devices poses significant challenges, including high computational and communication costs, slower-than-expected convergence, and reduced accuracy due to noisy encrypted gradient updates. This paper presents FedFlex, a novel framework for FL that remains privacy-preserving and adds homomorphic encryption (HE) along with an adaptive Dual-Factor Thompson Sampling (DFTS) adaptive client selection algorithm. DFTS identifies clients whose local model updates exhibit a strong correlation with the global model update while ensuring compatibility with the clients’ computational capabilities. In addition, we introduce a Noise-Reduced Gradient Flow that reduces the noise, which allows scaling the encrypted gradients to be efficiently transmitted while reducing the amount of noise and ciphertext. Extensive experiments conducted on multiple benchmark and real-world datasets show that FedFlex increases training accuracy by up to 95.9%, improves convergence speed by up to 3.2×, and decreases communication costs by 2×–4× relative to other existing approaches. These results showcase the flexibility and efficiency of FedFlex in improving secure FL for heterogeneous and resource-constrained IoT systems.

Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow. Traditional approaches often simplify road networks into standard graphs, which results in a failure to consider the dynamic nature of traffic data at neighboring intersections, thereby neglecting higher-order interconnections necessary for real-time control. To address this, we propose a novel TSCS framework to realize intelligent traffic control. This framework collaborates with multiple neighboring edge computing servers to collect traffic information across the road network. To elevate the efficiency of traffic signal control, we have crafted a multi-agent soft actor-critic (MA-SAC) reinforcement learning algorithm. Within this algorithm, individual agents are deployed at each intersection with a mandate to optimize traffic flow across the road network collectively. Furthermore, we introduce hypergraph learning into the critic network of MA-SAC to enable the spatio-temporal interactions from multiple intersections in the road network. This method fuses hypergraph and spatio-temporal graph structures to encode traffic data and capture the complex spatio-temporal correlations between multiple intersections. Our empirical evaluation, tested on varied datasets, demonstrates the superiority of our framework in minimizing average vehicle travel times and sustaining high-throughput performance. This work facilitates the development of more intelligent urban traffic management solutions.

Sleep stage classification is crucial for detecting patients’ health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for modelling non-Euclidean data, are unable to consider the heterogeneity and interactivity of multimodal data as well as the spatial-temporal correlation simultaneously, which hinders a further improvement of classification performance. In this paper, we propose a dynamic learning framework STHL, which introduces hypergraph to encode spatial-temporal data for sleep stage classification. Hypergraphs can construct multimodal/multi-type data instead of using simple pairwise between two subjects. STHL creates spatial and temporal hyperedges separately to build node correlations, then it conducts type-specific hypergraph learning process to encode the attributes into the embedding space. Extensive experiments show that our proposed STHL outperforms the state-of-the-art models in sleep stage classification tasks.

Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates the widespread implementation of artificial intelligence (AI)-based applications, especially for image processing tasks involving deep neural network (DNN). With the limited computing resources of an individual satellite, independently handling DNN tasks generated by diverse user equipments (UEs) becomes a significant challenge. One viable solution is dividing a DNN task into multiple subtasks and subsequently distributing them across multiple satellites for collaborative computing. However, it is challenging to partition DNN appropriately and allocate subtasks into suitable satellites while ensuring load balancing. To this end, we propose a collaborative satellite computing system designed to improve task processing efficiency in satellite networks. Based on this system, a workload-balanced adaptive task splitting scheme is developed to equitably distribute the workload of DNN slices for collaborative inference, consequently enhancing the utilization of satellite computing resources. Additionally, a self-adaptive task offloading scheme based on a genetic algorithm (GA) is introduced to determine optimal offloading decisions within dynamic network environments. The numerical results illustrate that our proposal can outperform comparable methods in terms of task completion rate, delay, and resource utilization.

Peer Reviewed

With the rapid development of artificial intelligence (AI) and the Internet of Things (IoT), intelligent information services have showcased unprecedented capabilities in acquiring and analysing information. The conventional task processing platforms rely on centralised Cloud processing, which encounters challenges in infrastructure-less environments with unstable or disrupted electrical grids and cellular networks. These challenges hinder the deployment of intelligent information services in such environments. To address these challenges, we propose a distributed task processing platform (${DTPP}$) designed to provide satisfactory performance for executing computationally intensive applications in infrastructure-less environments. This platform leverages numerous distributed homogeneous nodes to process the arriving task locally or collaboratively. Based on this platform, a distributed task allocation algorithm is developed to achieve high task processing performance with limited energy and bandwidth resources. To validate our approach,${DTPP}$has been tested in an experimental environment utilising real-world experimental data to simulate IoT network services in infrastructure-less environments. Extensive experiments demonstrate that our proposed solution surpasses comparative algorithms in key performance metrics, including task processing ratio, task processing accuracy, algorithm processing time, and energy consumption.

Drug substructure plays a crucial role in predicting drug-drug interaction (DDI) with combination drugs for disease therapies. In order to exploit the effect of drug substructure on DDI prediction, we propose a Key Substructure-aware Graph Transformer Network for Drug-drug Interaction Prediction (KSGTN-DDI). First, the substructure-adaptive graph Transformer module adaptively explicit encoding of drug structures information. Then, the key substructure-aware module calculates the importance of different substructures in DDI prediction. Finally, the calculated important substructure aggregation features are used to reconstruct the drug-drug interactions. Relevant experiments indicate that the performance of KSGTN-DDI outperforms other DDI prediction models.

The emergence of fog computing has brought unprecedented opportunities to the Internet-of-Things (IoT) field, and it is now feasible to incorporate deep learning at the edge of the IoT network to provide a wide range of highly tailored services. In this article, we present a fog-based democratically collaborative learning scheme in which fog nodes collaborate on the model training process even without the support of the cloud, contributing to the advances of IoT in terms of realizing a more intelligent edge. To achieve that, we design a voting strategy so that a fog node could be elected as the coordinator node based on both distance and computational power metrics to coordinate the training process. Also, a collaborative learning algorithm is proposed to generalize the training of different deep learning models in the fog-enabled IoT environment. We then implement two popular use cases, including a user trajectory prediction and a distributed image recognition, to demonstrate the feasibility, practicality, and effectiveness of the scheme. More importantly, the experiments on both use cases are conducted through a real world, in-door fog deployment. The result shows that the scheme can utilize fog to obtain a well-performing deep learning model in the cloudless IoT environment while mitigating the data locality issue for each fog node.

With the rapid development of IoT (Internet of Things) technology, numerous sensors are being deployed to the smart society with the integration of IoT services. Since the collected sensor data reflect the current status of the surrounding environment, determining accurate positions for sensors is crucial from the stage of setting the sensors to realize meaningful sensor data analysis. In this paper, we propose LESAR, a localization system for environmental sensing using augmented reality. LESAR uses a smartphone camera with the AR (Augmented Reality) function to measure the distances between sensors, while the ID of each sensor is identified simultaneously by analyzing the collected Bluetooth signals. The vision-based approach used can enable three-dimensional localization through the simple use of a smartphone.

Motivated by ubiquitous surveillance cameras in a smart city, a monitoring service can be provided to citizens. However, the rise of privacy concerns may disrupt this advanced service. Yet, the existing cloud-based services have not clearly proven that they can preserve Wth-privacy in which the relationship of three types of information, i.e., who requests the service, what the target is and where the camera is, does not leak. We address this problem by proposing a content-centric privacy platform (C2P2) that enables the construction of a Wth-privacy-preserving monitoring service without cloud dependency. C2P2 uses an image classification model of a target serving as the key to access the monitoring service specific to the target. In C2P2, communication is based on information-centric networking (ICN) that enables privacy preservation to be centered on the content itself rather than relying on a centralized system. Moreover, to preserve the privacy of bystanders, C2P2 separates the sensitive information (e.g., human faces) from the non-sensitive information (e.g., image background), while the privacy-aware forwarding strategies in C2P2 enable data aggregation and prevent privacy leakage resulting from false positive of image recognition. We evaluate the privacy leakage of C2P2 compared to that of the cloud-based system. The privacy analysis shows that, compared to the cloud-based system, C2P2 achieves a lower privacy loss ratio while reducing the communication cost significantly.

Building Energy Management System (BEMS) is a vital approach in constructing a global energy-efficient environment. It can be operated by analyzing data collected from sensors located in designated indoor areas. The key is to improve the data processing results while reducing the total data processing/communication volume required in the whole Internet of Things (IoT) networks as much as possible. In this work, a novel in-network self-learning algorithm for BEMS through a collaborative Fog platform is proposed. In particular, we devise an emerging Fog computing enabled IoT network architecture, where most of data can be processed in the Sensor-to-Fog and Fog-to-Fog layers. Data processing on Cloud is only required if anomalous sensor data are detected, and thus, the energy consumption due to heavy data processing on Cloud will be significantly reduced. The proposed algorithm makes the best use of Fog node capability to realize distributed data collection and processing. Via Fog-to-Fog connections, it can examine the sensor data by collecting them from different search ranges, whose values are meanwhile optimized. Numerical experiments conducted in a real indoor environment demonstrate that our algorithm achieve a high prediction accuracy for anomaly detection even with relatively small sensor data for processing. The effectiveness of Fog node placement is also verified. The overall scheme is expected to be a feasible solution to construct a cost-effective IoT network to minimize energy consumption while maximizing the indoor user's comfort, from the perspective of achieving a high prediction accuracy in BEMS data monitoring.

Fog computing is an emerging architecture for bringing processing, storage, and control from the Cloud closer to the Things/Users. Fog has mostly been studied in the vertical continuum between the Things/Users and the Cloud to provide resources traditionally existing in the remote Cloud to the applications. This paper introduces ICN-Fog, a novel horizontal Fog-to-Fog layer enabled by Information-Centric Networking. ICN-Fog enriches applications with horizontal data transfer in the Fog layer, distributed processing among Fog nodes, and built-in mobility support thanks to the smart connectionless name-based Fog-to-Fog data communications. We explain the rationale behind our design and demonstrate the advantages of the proposed Fog architecture through two representative case studies.