IEEE Transactions on Network Science and Engineering最新文献_第4页

Efficient Path Selection Design for Large Scale LEO Satellite Constellations Using Graph Embedding-Based Reinforcement Learning

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-18 DOI: 10.1109/TNSE.2025.3543161

Yuhan Kang;Yifei Zhu;Dan Wang;Zhu Han

{"title":"Efficient Path Selection Design for Large Scale LEO Satellite Constellations Using Graph Embedding-Based Reinforcement Learning","authors":"Yuhan Kang;Yifei Zhu;Dan Wang;Zhu Han","doi":"10.1109/TNSE.2025.3543161","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3543161","url":null,"abstract":"The rapid expansion of large-scale Low Earth Orbit (LEO) satellite constellations marks a new phase in global connectivity and communication. However, efficient path selection among numerous satellites connected by inter-satellite links (ISL) in such dynamic networks poses substantial challenges. This paper introduces a novel Path Selection Mechanism (PSM-LEO) utilizing Graph Embedding-Based Reinforcement Learning (GERL). Our GERL-based PSM-LEO method employs Graph Embedding (GE) to model the satellite network in a simplified low-dimensional space, simplifying the analysis of complex satellite relationships. Combining Reinforcement Learning (RL) with GE allows for dynamic adaptation of path choices in response to dynamic network conditions and communication needs. To the best of our knowledge, we are the first to propose GERL for PSM-LEO. We evaluate our method's performances through simulations in the Ansys Systems Tool Kit (STK), focusing on diverse LEO scenarios. Our results reveal that our approach surpasses several benchmarks in convergence speed, end-to-end latency, and energy consumption, demonstrating enhanced data transfer efficiency, reduced latency, and improved reliability. Additionally, we assess the scalability of the proposed method by analyzing its performance with increasing satellite constellation sizes. Our results confirm the framework's high scalability, demonstrating its suitability for addressing path selection challenges in future larger satellite networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2007-2020"},"PeriodicalIF":6.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Adaptive Masked Structural Learning for Graph-Level Clustering

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-18 DOI: 10.1109/TNSE.2025.3543194

Jinbin Yang;Jinyu Cai;Yunhe Zhang;Sujia Huang;Shiping Wang

{"title":"Towards Adaptive Masked Structural Learning for Graph-Level Clustering","authors":"Jinbin Yang;Jinyu Cai;Yunhe Zhang;Sujia Huang;Shiping Wang","doi":"10.1109/TNSE.2025.3543194","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3543194","url":null,"abstract":"Graph data presents a vast landscape for real-world applications. Current graph-level clustering approaches predominantly utilize graph neural networks to capture the intricate structural information for graph data. However, a significant challenge arises in effectively integrate structural and feature information under the prevalent noise in the real-world scenario. The advent of masking strategies has marked significant strides in boosting model robustness, accommodating incomplete data, and enhancing generalization capabilities. Yet, research attention on leveraging mask strategy for facilitating graph-level clustering is still limited. In this paper, we introduce a novel graph-level clustering method, towards adaptive masked structural learning for graph-level clustering. The method performs adaptive masking through reconstruction loss, and jointly adaptive mask representation learning and clustering in an end-to-end unsupervised framework. The mutual information between maximized the entire graph and substructure representations is also utilized to learn to generate cluster-oriented graph-level representations. Extensive experiments on eight real graph-level benchmark datasets demonstrate the effectiveness and superiority of the proposed method.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2021-2032"},"PeriodicalIF":6.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maximizing Entanglement Routing Rate in Quantum Networks: Approximation Algorithms

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-18 DOI: 10.1109/TNSE.2025.3542332

Dung H. P. Nguyen;Ethan Hunt;Dillon J. Horton;Tu N. Nguyen;Bing-Hong Liu

{"title":"Maximizing Entanglement Routing Rate in Quantum Networks: Approximation Algorithms","authors":"Dung H. P. Nguyen;Ethan Hunt;Dillon J. Horton;Tu N. Nguyen;Bing-Hong Liu","doi":"10.1109/TNSE.2025.3542332","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3542332","url":null,"abstract":"There will be a fast-paced shift from conventional network systems to novel quantum networks that are supported by the quantum entanglement and teleportation, key technologies of the quantum era, to enable secured data transmissions in the next-generation of the Internet. Despite this prospect, migration to quantum networks cannot happen all at once, especially when it comes to quantum routing. In this paper, we focus on the maximizing entanglement routing rate (MERR) problem, which aims to determine entangled routing paths for the maximum number of demands in the quantum network while meeting the network's fidelity. To tackle this problem, we first formulate the MERR problem using an integer linear programming (ILP) model. We then leverage the method of linear programming relaxation to devise two efficient algorithms, including the half-based rounding algorithm (HBRA) and the randomized rounding algorithm (RRA) with a provable approximation ratio for the objective function. Furthermore, to address the challenge of the combinatorial optimization problem in big scale networks, we also propose the path-length-based approach (PLBA) to solve the MERR problem. Finally, we evaluate the performance of our algorithms and show up the success of maximizing the entanglement routing rate.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1939-1952"},"PeriodicalIF":6.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Optimal Charging Strategy for Battery Swapping Stations Under Time-of-Use Pricing

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-18 DOI: 10.1109/TNSE.2025.3543449

Huanyu Yan;Chenxi Sun;Huanxin Liao;Xiaoying Tang

{"title":"Real-Time Optimal Charging Strategy for Battery Swapping Stations Under Time-of-Use Pricing","authors":"Huanyu Yan;Chenxi Sun;Huanxin Liao;Xiaoying Tang","doi":"10.1109/TNSE.2025.3543449","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3543449","url":null,"abstract":"Battery Swapping Stations (BSSs), the emerging infrastructure for electric vehicles (EVs), are swiftly proliferating facilities bridging energy and transportation networks. As the power grid's demand-side-management approach evolves, the optimal charging strategy for competitive BSSs needs further investigation. This paper proposes a real-time optimal charging strategy for each non-cooperative BSS operating under a unified power grid that implements Time-of-use (TOU) pricing. We construct a non-cooperative game model to encapsulate the competition among BSSs under the EV reservation mechanism. To resolve the game, we prove the existence of a unique Nash Equilibrium under any number of players and swapping prices, and design an algorithm to solve the equilibrium. Additionally, we suggest strategies for EVs without reservations. Specifically, we demonstrate the conditions under which the BSS profit diminishes when serving directly drive-in EVs. We also establish that the potential cost arising from no-show reserved EVs is limited by a constant. Simulations validate that our proposed battery charging strategy significantly enhances the profits of a 12-station BSS system. Moreover, the real-time optimal charging strategy also accomplishes peak-shaving over multiple time periods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2043-2056"},"PeriodicalIF":6.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Encryption-Decryption-Based Bounded Filtering for 2-D Systems Under Dynamic Event-Triggered Mechanism

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-18 DOI: 10.1109/TNSE.2025.3542177

Pan Zhang;Chaoqun Zhu;Bin Yang;Bohan Zhang;Zhiwen Wang

引用次数: 0

Queue-Based Analytical Modeling for Capacity Estimation of Wearable eHealth Systems

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-18 DOI: 10.1109/TNSE.2025.3543376

Nidhi Pathak;Anandarup Mukherjee;Sudip Misra

{"title":"Queue-Based Analytical Modeling for Capacity Estimation of Wearable eHealth Systems","authors":"Nidhi Pathak;Anandarup Mukherjee;Sudip Misra","doi":"10.1109/TNSE.2025.3543376","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3543376","url":null,"abstract":"The heterogeneity in IoT-based health monitoring devices and communication protocols leads to diverse system designs and configurations resulting in uncertain outcomes. Most present-day eHealth approaches focus on optimizing the performance of already designed systems (hardware, network, and software) rather than optimizing the system design itself. A reliable eHealth monitoring system must use appropriate protocols and hardware to avoid data loss and delays. Identifying the magnitude of performance changes when integrating new devices into an already functional eHealth system is also critical. This work addresses these issues by adopting a queuing theory-based analytical model to characterize an eHealth wearable system. This approach acts as a guiding framework for system and network capacity estimation by simulating the dynamics of an eHealth wearable system with selected communication protocols before deployment. The simulation tool identifies the system's limits in terms of delays, the number of simultaneously supported wearables, and packet loss. We identify and examine some well-known wireless connectivity protocols as possible candidates for eHealth wearable systems. The output of the analytical model is compared vis-à-vis data from a real-life proof-of-concept eHealth wearable system. The proposed approach estimates the numbers of served packets and blocked packets with approximate errors of 0.02% and 0.2%, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2033-2042"},"PeriodicalIF":6.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Task Load Identification and Signal Denoising via Hierarchical Knowledge Distillation

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-17 DOI: 10.1109/TNSE.2025.3542409

Jiahao Jiang;Zhelong Wang;Sen Qiu;Xiang Li;Chenming Zhang

{"title":"Multi-Task Load Identification and Signal Denoising via Hierarchical Knowledge Distillation","authors":"Jiahao Jiang;Zhelong Wang;Sen Qiu;Xiang Li;Chenming Zhang","doi":"10.1109/TNSE.2025.3542409","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3542409","url":null,"abstract":"Complex neural networks with deep structures are beneficial for solving problems such as load classification in Non-intrusive load monitoring (NILM) due to their powerful feature extraction capabilities. Unfortunately, corresponding complex models designed based on deep learning algorithms require high computational and memory resources. Additionally, the external noise interference during practical load identification poses a challenge. To solve these difficulties with practical industrial significance, this paper proposes a multi-task-knowledge distillation (MTL-KD) framework for NILM. The main contributions within this framework include a new feature extraction method that combines variational mode extraction (VME) and mutual information (MI) to extract unique features and filter out noise interference, an attention-based MTL model to simultaneously perform the load identification and signal de-noising tasks, and new KD modules to transfer knowledge from a complex teacher model to a small student model. Experimental evaluations conducted on public datasets such as the plug-load appliance identification dataset (PLAID) and the worldwide household and industry transient energy dataset (WHITED), as well as a private load dataset collected in the lab, demonstrate that the proposed MTL-KD framework surpasses state-of-the-art approaches.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1967-1980"},"PeriodicalIF":6.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Target Detection in Underwater Sensor Networks Based on Bayesian Deep Learning

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-13 DOI: 10.1109/TNSE.2025.3535572

Xiaoli Du;Yintang Wen;Jing Yan;Yuyan Zhang;Xiaoyuan Luo;Xinping Guan

{"title":"Multi-Target Detection in Underwater Sensor Networks Based on Bayesian Deep Learning","authors":"Xiaoli Du;Yintang Wen;Jing Yan;Yuyan Zhang;Xiaoyuan Luo;Xinping Guan","doi":"10.1109/TNSE.2025.3535572","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3535572","url":null,"abstract":"Underwater target detection and its development have an important role in advancing marine science and technology. However, the complex and dynamic underwater environment poses challenges for detecting non-cooperative targets. This paper focuses on the problem of detecting and recognizing multiple non-cooperative targets in USNs. Specifically, the generative model is firstly utilized to learn the probability distribution of underwater signals, and then Bayesian fusion of active and passive measurements is utilized to achieve target detection. Along with this, a Bayesian deep learning classification framework is employed to categorize multiple targets. Compared to the traditional statistical detection methods, our method excels in hading underwater complexity and dynamics. In addition, unlike traditional deep learning, our classification framework combines Bayesian inference with deep learning to quantify environmental uncertainty. This approach helps the model perform more robust detection and improves the management of noise and uncertainty. Experimental and simulation analysis demonstrate the effectiveness of Bayesian deep learning methods in solving the challenges of underwater target detection. These findings highlight the potential of our approach in enhancing sensing and surveillance capabilities in complex underwater environments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1581-1596"},"PeriodicalIF":6.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Hamiltonian Property of the Data Center Network DPCell

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-13 DOI: 10.1109/TNSE.2025.3537698

Hui Dong;Huaqun Wang;Mengjie Lv;Weibei Fan

{"title":"The Hamiltonian Property of the Data Center Network DPCell","authors":"Hui Dong;Huaqun Wang;Mengjie Lv;Weibei Fan","doi":"10.1109/TNSE.2025.3537698","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3537698","url":null,"abstract":"Data center networks (DCNs) play an irreplaceable role in digital transformation by enabling data storage, processing, and complex computing. DPCell is a DCN built on a novel fabric of switches structure, outperforming other DCNs based on dual-port servers in scalability and bisection width. Ensuring reliable communication performance in DCNs is critical for continuous service delivery. In this paper, we investigate the reliable communication performance of DPCell from the perspective of Hamiltonian properties. We first prove that DPCell is Hamiltonian-connected. Considering the inevitability of network failures, we further prove that DPCell is a super fault-tolerant Hamiltonian network under a fault model, achieving an optimal result. The superior Hamiltonian properties of DPCell confirm its reliable communication performance, laying the foundation for deadlock-free reliable communication and fast adaptive diagnosis. We verify the theoretical results through simulation experiments and further evaluate DPCell's Hamiltonian properties under conditions exceeding the fault model limit, including structure faults caused by network attacks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1660-1676"},"PeriodicalIF":6.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Early Diabetic Retinopathy Cyber-Physical Detection System Using Attention-Guided Deep CNN Fusion

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-13 DOI: 10.1109/TNSE.2025.3541138

M. Shamim Hossain;Mohammad Shorfuzzaman

{"title":"Early Diabetic Retinopathy Cyber-Physical Detection System Using Attention-Guided Deep CNN Fusion","authors":"M. Shamim Hossain;Mohammad Shorfuzzaman","doi":"10.1109/TNSE.2025.3541138","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3541138","url":null,"abstract":"Diabetic retinopathy is the most common and severe eye complication of diabetes, and it can cause vision loss or even blindness due to retina damage. Automatic and faster detection of various DR stages is crucial and can benefit both patients and ophthalmologists. With the ubiquity of measurement devices and growing processing power, cyber-physical characterization is becoming an enabler technology in many disciplines. This paper proposes a cyber-physical system (CPS) framework that will aid clinicians in establishing an early diagnosis of DR. Particularly; we present a component-based CPS architecture to use a deep learning-based predictive model deployed on the cloud for effective DR diagnosis and incorporate medical devices. To this end, a deep learning-based explainable CNN fusion model has been introduced in the proposed framework for automatic screening and interpretation of DR stages using digital fundus images. We extract salient features using various fine-tuned CNN models in conjunction with an attention network, and we use a locally connected layer to calculate the weighted contribution of these networks. We measure the performance of our approach using a public dataset comprising fundus images of five different categories. Experimental results demonstrate the proposed approach's effectiveness for faster and more accurate detection of various DR stages.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1898-1910"},"PeriodicalIF":6.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0