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FCLA-DT: Federated Continual Learning with Authentication for Distributed Digital Twin-based Industrial IoT

Journal of Communications and Information Networks 2025年第4期9卷 362-373页

作者： Xia, Yingjie Liu, Xuejiao Zhao, Yunxiao Wang, Yun Micro-Electronics Research Institute Hangzhou Dianzi University Hangzhou310005 China School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China School of Cyberspace Security Technology Hangzhou Dianzi University Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310012 China Hangzhou Yuantiao Technology Company Hangzhou310036 China

Digital twin (DT) technology is currently pervasive in industrial Internet of things (IoT) appli-cations, notably in predictive maintenance scenarios. Prevailing digital twin-based predictive maintenance methodologies are constrained by a narrow focus on singular physical modeling paradigms, impeding comprehensive analysis of diverse factory data at scale. This paper introduces an improved method, federated continual learning with authentication for distributed digital twin-based industrial IoT (FCLA-DT). This decentralized strategy ensures the continual learning capability vital for adaptive and real-time decision-making in complex industrial predictive maintenance systems. An authentication scheme based on group signature is introduced to enable the verification of digital twin identities during inter-twin collaborations, avoiding unauthorized access and potential model theft. Security analysis shows that FCLA-DT can enable numerous nodes to collaborate learning without compromising individual twin privacy, thereby ensuring group authentication in the cooperative distributed industrial IoT. Performance analysis shows that FCLA-DT outperforms traditional federated learning methods with over 95% fault diagnosis accuracy and ensures the privacy and authentication of digital twins in multi-client task learning. © 2025, Posts and Telecom Press Co Ltd. All rights reserved.

关键词： Federated learning

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An improved estimation method of secondary path uncertainties for feedback active noise control earphones

Noise Control Engineering Journal

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Noise Control Engineering Journal 2024年第3期72卷 166-174页

作者： Guo, Jianfeng Xun, Ziyou Ren, Zhenxing Yin, Lan Wang, Han The College of Computer Science and Technology College of Data Science Taiyuan University of Technology Taiyuan030024 China The Key Laboratory of Noise and Vibration Research Institute of Acoustics Chinese Academy of Sciences Beijing100190 China

The fixed feedback control has been widely used for active noise-canceling earphones due to its simple structure and higher performance for low-frequency noise reduction. Although some disk-based models have been done to design robust controllers, they usually overestimate the uncertainty and thus limit noise reduction. In this article, we analyze the distribution of measured secondary paths and propose the tri-rectangle model to provide the uncertainty bound. The cascade biquad controllers are optimized by solving the multi-constrained optimization problem, with H2 performance criteria and H1 performance criteria and constraints of secondary path uncertainty and noise amplification. Simulation and experiment results show that the proposed tri-rectangle model provides a more accurate estimate of the uncertainty than disk-based models, and the designed controller achieves good noise attenuation with acceptable noise enhancement and strong stability. © 2024 institute of Noise Control Engineering.

关键词： Controllers

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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DetSpace:Distributed Congestion Free Routing for Global Deterministic Network in Mega-Constellations

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China Communications 2024年第12期21卷 203-216页

作者： Li Jihao Li Hewu Lai Zeqi Wang Xiaomo Beijing National Research Center for Information Science and Technology(BNRist) Beijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Institute of Network Science and Cyberspace Tsinghua UniversityBeijing 100084China Zhongguancun Laboratory Beijing 100095China China Academy of Electronics and Information Technology Beijing 100040China

Emerging long-range industrial IoT applications(e.g.,remote patient monitoring)have increasingly higher requirements for global deterministic *** many existing methods have built deterministic networks in small-scale networks through centralized computing and resource reservation,they cannot be applied on a global *** emerging mega-constellations enable new opportunities for realizing deterministic delay *** one constellation(e.g.,Starlink)might be managed by a single operator(e.g.,SpaceX),packets can be routed within deterministic number of ***,the path diversity brought by the highly symmetrical network structure in mega-constellations can help to construct a congestion free network by *** paper leverages these unique characteristics of mega-constellations to avoid the traditional network congestion caused by multiple inputs and single output,and to determine the routing hops,and thus realizing a global deterministic network(DETSPACE).The model based on the 2D Markov chain theoretically verifies the correctness of *** effectiveness of DETSPACE in different traffic load con-ditions is also verified by extensive simulations.

关键词： congestion free deterministic network mega-constellations path diversity

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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E-voting system using cloud-based hybrid blockchain technology

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Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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An Autoencoder-BiLSTM framework for classifying multiple types of lung diseases from CXR images

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Multimedia Tools and Applications 2024年第38期83卷 86091-86120页

作者： Ankayarkanni, B. Sangeetha, P. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai600 119 India Rajiv Gandhi College of Engineering Research and Technology Maharashtra Chandrapur442403 India

Millions of people die from lung illness each year as a result of its rise in recent years. CXR imaging is one of the most widely used and reasonably priced diagnostic techniques for the diagnosis of many illnesses. Unfortunately, even for seasoned radiologists, accurately diagnosing sickness from Chest X-Rays (CXR) samples is challenging. To combat the pandemic, a reliable, affordable, and efficient way to diagnose lung disease has become essential. Consequently, a unique optimized Auto Encod-BI Long-Short Term Memory (Bi-LSTM) model is proposed in this research work. Pre-processing, segmentation, feature extraction, and multiple types of lung illness diagnosis are the four main stages of the suggested model. First, Laplacian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to pre-process the gathered CXR pictures. Next, the Region of Interest (ROI) from the previously processed images are recognized by means of the newly enhanced MobileNetV2. The new Self-Improved Slime Mould Algorithm (SI-SMA) is used to fine-tune the hyper-parameters of MobileNetV2 in order to precisely identify the afflicted locations. Based on the phenomenon of slime mould oscillation, the conventional Slime Mould Algorithm (SMA) model has been modified with the creation of the SI-SMA model. Next, characteristics like the Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) are taken out. Finally, a unique AutoEncod-BiLSTM Framework—which is divided into three categories—is shown to automate the process of identifying illnesses in CXR pictures: pneumonia, COVID-19, and normal. The autoencoder and Bi-LSTM are combined to create the suggested AutoEncod-BiLSTM model. The retrieved features are used to train the AutoEncod-BiLSTM Framework. Moreover, the proposed model enhanced the disease detection efficiency than the existing models and the disease detection accuracy of the proposed model is about 99.1%. Furthermore, the suggested model attains better

关键词： Equalizers

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Intelligent networking in adversarial environment:challenges and opportunities

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science China(Information sciences) 2022年第7期65卷 5-15页

作者： Yi ZHAO Ke XU Qi LI Haiyang WANG Dan WANG Min ZHU Department of Computer Science and Technology Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Institute for Network Sciences and Cyberspace Tsinghua University Department of Computer Science University of Minnesota Duluth Department of Computing The Hong Kong Polytechnic University

Although deep learning technologies have been widely exploited in many fields, they are vulnerable to adversarial attacks by adding small perturbations to legitimate inputs to fool targeted ***, few studies have focused on intelligent networking in such an adversarial environment, which can pose serious security threats. In fact, while challenging intelligent networking, adversarial environments also bring about opportunities. In this paper, we, for the first time, simultaneously analyze the challenges and opportunities that the adversarial environment brings to intelligent networking. Specifically, we focus on challenges that the adversarial environment will pose on the existing intelligent networking. Furthermore, we investigate frameworks and approaches that combine adversarial machine learning with intelligent networking to solve the existing deficiencies of intelligent networking. Finally, we summarize the issues, including opportunities and challenges, which can allow researchers to focus on intelligent networking in adversarial environments.

关键词： intelligent networking adversarial attacks defense security

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An Encrypted Malicious Traffic Detection Method Based on Deeper Protocol Features 9

An Encrypted Malicious Traffic Detection Method Based on Dee...

引用

9th IEEE International Conference on Data science in cyberspace, DSC 2024

作者： Chen, Lei Chen, Yilu Liu, Chenwei Feng, Wenying National University of Defense Technology College of Computer Science and Technology Changsha China Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China Pengcheng Laboratory Department of New Networks Shenzhen China

ISBN: (纸本)9798350391367

Network encryption protocols can protect user privacy and data security but can also provide an opportunity to conceal network attacks. To address feature loss and incompleteness in existing encrypted malicious traffic detection methods, this paper proposes a LightGBM-based detection method. By extracting statistical features and constructing deep TLS protocol features, it achieves high-precision detection for malicious encrypted traffic. In this method, statistical features are selected and calculated from traffic packet distributions, and TLS protocol features are constructed based on protocol knowledge and network business background. The combined features are input into a LightGBM model after using TargetEncoder to handle high-dimensional sparse features. Results of malicious and benign traffic distribution show that statistical and protocol features we selected and constructed have significant differences, proving that they are efficient for encrypted malicious traffic detection. © 2024 IEEE.

关键词： Differential privacy

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