IEEE Transactions on Network Science and Engineering最新文献

{"title":"Optimizing Energy Consumption and Coverage in Underwater Magnetic Induction-Assisted Acoustic WSNs Using Learning Automata-Based Cooperative MIMO Formation","authors":"Qingyan Ren;Yanjing Sun;Sizhen Bian;Michele Magno","doi":"10.1109/TNSE.2025.3561751","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3561751","url":null,"abstract":"Underwater Wireless Sensor Networks (UWSNs) offer promising exploration capabilities in challenging underwater environments, necessitating a focus on reducing energy consumption while guaranteeing monitoring coverage. Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input–multiple-output (MIMO) WSNs have shown advantages over traditional UWSNs in various aspects due to the seamless integration of sensor networks and communication technology. However, as an emerging topic, a critical gap exists, as they often overlook the vital considerations of monitoring coverage requirements and the dynamic nature of the unknown underwater environment. Moreover, these advantages can be further enhanced by harnessing the collaborative potential of multiple independent underwater nodes. This paper introduces a significant advancement to the field of MI-assisted Acoustic Cooperative MIMO WSNs leveraging the innovative Confident Information Coverage (CIC) and a reinforcement learning paradigm known as Learning Automata (LA). The paper presents the LA-based Cooperative MIMO Formation (LACMF) algorithm designed to minimize communication energy consumption in sensors while concurrently maximizing coverage performance. Experimental results demonstrate the LACMF considerably outperforms other schemes in terms of energy consumption, and network coverage to satisfy the imposed constraints, the CIC can be improved up to by an additional 52%, 11% reduction in energy consumption.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3527-3540"},"PeriodicalIF":7.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891284","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}

{"title":"Retraction Notice: Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network","authors":"Prabhat Kumar;Randhir Kumar;Abhinav Kumar;A. Antony Franklin;Sahil Garg;Satinder Singh","doi":"10.1109/TNSE.2025.3583800","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3583800","url":null,"abstract":"The rapid expansion of the Industrial Internet of Things (IIoT) necessitates the digitization of industrial processes in order to increase network efficiency. The integration of Digital Twin (DT) with IIoT digitizes physical objects into virtual representations to improve data analytics performance. Nevertheless, DT empowered IIoT generates a massive amount of data that is mostly sent to the cloud or edge servers for real-time analysis. However, unreliable public communication channels and lack of trust among participating entities causes various types of threats and attacks on the ongoing communication. Motivated from the aforementioned discussion, we present a blockchain and Deep Learning (DL) integrated framework for delivering decentralized data processing and learning in IIoT network. The framework first present a new DT model that facilitates construction of a virtual environment to simulate and replicate security-critical processes of IIoT. Second, we propose a blockchain-based data transmission scheme that uses smart contracts to ensure integrity and authenticity of data. Finally, the DL scheme is designed to apply the Intrusion Detection System (IDS) against valid data retrieved from blockchain. In DL scheme, a Long Short Term Memory-Sparse AutoEncoder (LSTMSAE) technique is proposed to learn the spatial-temporal representation. The extracted characteristics are further used by the proposed Multi-Head Self-Attention (MHSA)-based Bidirectional Gated Recurrent Unit (BiGRU) algorithm to learn long-distance features and accurately detect attacks. The practical implementation of our proposed framework proves considerable enhancement of communication security and data privacy in DT empowered IIoT network.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"4329-4329"},"PeriodicalIF":7.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction Notice: A DQN-Based Frame Aggregation and Task Offloading Approach for Edge-Enabled IoMT","authors":"Xiaoming Yuan;Zedan Zhang;Chujun Feng;Yejia Cui;Sahil Garg;Georges Kaddoum;Keping Yu","doi":"10.1109/TNSE.2025.3582063","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3582063","url":null,"abstract":"The rapid expansion of wearable medical devices and health data of Internet of Medical Things (IoMT) poses new challenges to the high Quality of Service (QoS) of intelligent health care in the foreseeable 6 G era. Healthcare applications and services require ultra reliable, ultra low delay and energy consumption data communication and computing. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies empowered IoMT to deal with huge data sensing, processing and transmission in high QoS. However, traditional frame aggregation schemes in WBAN generate too much control frames during data transmission, which leads to high delay and energy consumption and is not flexible enough. In this paper, a Deep Q-learning Network (DQN) based Frame Aggregation and Task Offloading Approach (DQN-FATOA) is proposed. Firstly, different service data were divided into queues with similar QoS requirements. Then, the length of the frame aggregation was selected dynamically by the aggregation node according to the delay, energy consumption, and throughput by DQN. Finally, the number of tasks offloaded was selected due to the current state. Compared with the traditional scheme, the simulation results show that the proposed DQN-FATOA has effectively reduced delay and energy consumption, and improved the throughput and overall utilization of WBAN.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"4330-4330"},"PeriodicalIF":7.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11048693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Cooperation Control for Multiple Urban Regions Traffic Flow Coupled With an Expressway Network","authors":"Yunran Di;Weihua Zhang;Heng Ding;Haotian Shi;Junwei You;Hangyu Li;Bin Ran","doi":"10.1109/TNSE.2025.3580759","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3580759","url":null,"abstract":"As cities expand and long-distance travel demand increases, expressways are usually constructed to enhance regional connectivity. In the mixed road networks of urban roads and expressways, coordinating the distinct traffic dynamics of the two networks is a challenge. To address this challenge, we propose a cooperative flow control method for large-scale mixed networks. First, we develop an integrated traffic model that models urban regions using the macroscopic fundamental diagram (MFD) and expressways using the multi-class cell transmission model (CTM), achieving route tracking of vehicles throughout the entire mixed network. Next, a route choice model is developed to allocate new traffic demands within the mixed network. To coordinate traffic flow, a perimeter control (PC) is conducted to manage transfer flows between region boundaries, ramp metering (RM) to regulate flows entering expressways from urban regions, and variable speed limit (VSL) to control mainline speeds on expressways. We establish this cooperative flow control method within a model predictive control (MPC) framework. Case studies show that, based on the implementation of PC, the combined application of RM and VSL to the expressway system is more effective in reducing congestion and improving traffic efficiency in the mixed network than using RM and VSL independently.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"5058-5072"},"PeriodicalIF":7.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352031","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}

{"title":"Online Satellite Selection, Request Dispatching, and Service Provisioning in LEO Edge Constellations","authors":"Siru Chen;Lei Jiao;Konglin Zhu;Lin Zhang","doi":"10.1109/TNSE.2025.3578194","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3578194","url":null,"abstract":"Facing the growing demand for Low Earth Orbit (LEO) edge services, in order to address the manageability and economic issues in LEO edge computing, this study introduces an innovative two-timescale optimization approach designed to dynamically optimize satellite access selection, user request dispatching, and service replica placement. Integrating both online and offline optimizations, our method adapts to real-time fluctuations in user demand and satellite resources, effectively managing long-term decisions such as service migration and replica placement. We formalize this optimization challenge as a finite-horizon, integer-variable problem, taking into account both switching costs and resource utilization. Through extensive experimentation, our approach is proven to significantly balance performance enhancement and resource efficiency, and we prove the approximation ratio for each time slot the competitive ratio for the long-term cost. Our work contributes to the understanding of multi-timescale optimization in LEO edge computing and provides valuable insights for designing efficient control mechanisms in satellite-based systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4952-4966"},"PeriodicalIF":7.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352143","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}

{"title":"Heapsort-Based Secure Top-K Query Scheme for E-Healthcare Systems","authors":"Wenjing Yang;Hao Wang;Zhi Li;Ziyu Niu;Ye Su","doi":"10.1109/TNSE.2025.3580800","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3580800","url":null,"abstract":"In the e-Healthcare ecosystem, medical institutions increasingly rely on cloud computing platforms to outsource data storage and query processing, aiming to optimize service delivery efficiency. A critical component of such services is the top-<inline-formula><tex-math>$k$</tex-math></inline-formula> query, which identifies the <inline-formula><tex-math>$k$</tex-math></inline-formula> most relevant or highest-ranked records within datasets. However, since medical data contain sensitive patient information and require privacy-preserving outsourcing, traditional top-<inline-formula><tex-math>$k$</tex-math></inline-formula> query schemes are no longer suitable, while existing privacy-preserving solutions suffer from high computational overhead in practice. To address these issues, we introduce a lightweight privacy-preserving secure top-<inline-formula><tex-math>$k$</tex-math></inline-formula> query scheme. Specifically, our proposed scheme utilizes lightweight cryptographic tools, additive secret sharing and Function Secret Sharing (FSS) techniques, as the foundation of the underlying distributed secure computation. These techniques significantly reduce computational overhead while ensuring the privacy of medical data. Furthermore, we propose a Secure Max Heap Sorting (MH) protocol, which helps us to rapidly implement the top-<inline-formula><tex-math>$k$</tex-math></inline-formula> query functionality in our scheme. Additionally, we design a set of fundamental secure protocols based on FSS, including the Secure Minimum Value (MinV) protocol, Secure Maximum Value (MaxV) protocol and Secure Heap Adjustment (HA) protocol. By integrating our cryptographic protocols with a secure squared Euclidean distance protocol, we construct a secure top-<inline-formula><tex-math>$k$</tex-math></inline-formula> query scheme for e-Healthcare scenarios. Finally, we present formal security proofs under the semi-honest adversary model, which theoretically establish the security of the proposed scheme. Thanks to the adoption of secret sharing techniques, our scheme requires the client to only split the data into secret shares, a process that incurs nearly zero computational cost. The superior efficiency of our solution is further demonstrated through theoretical analysis and experimental evaluations.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"5073-5085"},"PeriodicalIF":7.9,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352210","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}

{"title":"Semi-Supervised Adaptive Symmetric Nonnegative Matrix Factorization for Multi-View Clustering","authors":"Mehrnoush Mohammadi;Kamal Berahmand;Shadi Azizi;Razieh Sheikhpour;Hassan Khosravi","doi":"10.1109/TNSE.2025.3578315","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3578315","url":null,"abstract":"Multi-view clustering (MVC) has gained attention for its ability to efficiently handle complex high-dimensional data. Many existing MVC methods rely on a technique known as Nonnegative Matrix Factorization (NMF). Among these, Symmetric Nonnegative Matrix Factorization (SNMF) notably stands out for its ability to reduce dimensionality and provide easily interpretable representations. However, existing research highlights several challenges associated with SNMF. Firstly, it often necessitates the manual creation of the similarity matrix, which can be effort-intensive. Additionally, SNMF intrinsically employs an unsupervised learning approach, thus inherently neglecting the potential utility of label information. Lastly, while it concentrates on identifying shared information within multi-view data, it tends to overlook the valuable insights that different views might individually present. To overcome these limitations, we propose a novel semi-supervised multi-view clustering framework, termed Semi-supervised Adaptive Symmetric NMF (SSA-SNMF), which integrates adaptive learning and supervision into the SNMF model. The proposed method incorporates three essential components into its objective function: (1) adaptive similarity matrix construction to automatically capture data relationships, (2) integration of pairwise constraint information to leverage available supervision, and (3) a fusion mechanism that balances complementary and consensus information across views. We also derive an efficient optimization algorithm with convergence guarantees. Experimental results on six benchmark datasets show that SSA-SNMF consistently outperforms six state-of-the-art methods, demonstrating its effectiveness and robustness for multi-view clustering tasks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4967-4981"},"PeriodicalIF":7.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351983","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}

{"title":"Advancements in Epidemic Transmission Suppression: A Comprehensive Survey","authors":"Lili Tong;Shan Zhang;Hao Sun;Yuliang Cai;Jiawei Zhang;Wei Qian;Qingchao Zhang;Qiang He;Junxin Chen;Jia Li","doi":"10.1109/TNSE.2025.3579136","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3579136","url":null,"abstract":"The large-scale spread of epidemics poses a serious threat to human life and health security. It disrupts educational development, cripples the global economy, triggers social unrest, and jeopardizes global stability. Consequently, understanding how to rapidly comprehend the transmission of epidemics and control their outbreaks has become a prominent topic in the field of epidemiology. This paper provides a comprehensive overview of the application of computer technology in epidemiological research, categorizing the challenge of curbing epidemic spread into three key areas: 1) epidemic transmission, 2) epidemic source tracing, and 3) epidemic suppression strategies. Each area is thoroughly reviewed and summarized. First, in the context of epidemic transmission, this paper summarizes various transmission models used to describe and predict the spread of epidemics within populations. Second, regarding epidemic source tracing, the paper reviews relevant studies from the perspectives of single-source and multi-source detection. Third, in terms of epidemic suppression strategies, it offers a detailed overview of diverse approaches aimed at reducing the spread and prevalence of diseases among populations. Finally, the paper discusses the challenges faced in the field of epidemic suppression and explores potential future directions for research and development.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"5024-5044"},"PeriodicalIF":7.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352000","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}

{"title":"Content-Aware AP Selection With LSTM-Enabled Proactive Caching in Cell-Free Massive MIMO Networks","authors":"Mahnoor Ajmal;Seri Park;Malik Muhammad Saad;Muhammad Ashar Tariq;Dongkyun Kim","doi":"10.1109/TNSE.2025.3578687","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3578687","url":null,"abstract":"Cell-Free massive MIMO (CF-mMIMO) networks face significant challenges in achieving Ultra-Reliable Low-Latency Communication (URLLC) requirements due to inherent delays in content retrieval from central processing units (CPUs). This paper presents an integrated framework that jointly optimizes access point (AP) selection and content caching to minimize latency while maintaining reliability. We develop a novel content-aware user-centric clustering scheme that considers both cached content availability and channel conditions. The scheme features a Content Query Beacon (CQB) mechanism, which verifies content availability prior to connection establishment. To address the dynamic nature of content popularity, we design a novel proactive content caching strategy using Long Short-Term Memory (LSTM) to minimize CPU-dependent data retrieval. Extensive simulations demonstrate that our proposed framework achieves a 75% reduction in content delivery latency, 31.87% improvement in Quality of Experience (QoE), and a 26.8% increase in cache hit rates compared to conventional approaches. This comprehensive solution significantly enhances the capability of CF-mMIMO networks to deliver URLLC services, particularly in densely populated areas with diverse content demands.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4982-4997"},"PeriodicalIF":7.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352166","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}

{"title":"On Random Seeding for Influence Maximization in Differentially Private Graphs: Balancing Privacy and Utility Using Percolation Theory","authors":"Niranjana Unnithan;Balasubramaniam Natarajan;George Amariucai","doi":"10.1109/TNSE.2025.3578801","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3578801","url":null,"abstract":"Influence maximization in social networks is a fundamental problem in network science with applications in viral marketing, information diffusion, and opinion formation. However, privacy concerns pose a significant challenge while designing strategies to maximize the spread of influence. In this paper, we study influence maximization under differential privacy constraints by considering two graph perturbation mechanisms: edge addition and edge addition/ deletion. We demonstrate that random seeding along with carefully crafted graph perturbation mechanisms achieve effective diffusion outcomes while preserving privacy. This approach leverages percolation theory to show that graph perturbation diminishes the value of network information, making random seeding asymptotically comparable to conventional optimization techniques in certain percolation phases. We provide theoretical proofs and experimental validations demonstrating the effectiveness of our approaches. Our methods offer a robust solution to the trade-off between privacy and utility in influence maximization, opening avenues for privacy-preserving strategies in social network analysis.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4998-5011"},"PeriodicalIF":7.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352161","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}