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Navigating the Digital Twin Network landscape:A survey on architecture,applications,privacy and security

High-Confidence Computing 2024年第4期4卷 129-149页

作者： Akshita Maradapu Vera Venkata Sai Chenyu Wang Zhipeng Cai Yingshu Li Department of Computer and Information Sciences Towson UniversityTowson21252USA Department of Software Engineering and Game Development Kennesaw State UniversityMarietta30060USA Department of Computer Science Georgia State UniversityAtlanta30303USA

In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity *** has led to the development and commercialization of Digital Twin(DT)*** widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical *** create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT *** high volume of user data and the ubiquitous communication systems in DTNs come with their own set of *** most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data ***,currently,there is not enough literature that focuses on privacy and security issues in DTN *** this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their ***,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective *** then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the *** also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the ***,we provide some open research issues and problems in the field of DTN privacy and security.

关键词： Digital Twin Networks Network architecture Privacy and security Federated learning Blockchain Internet of Vehicles 6G Supply chain

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Comparative review of the surround speaker virtualizers on DSP platform 32

Comparative review of the surround speaker virtualizers on D...

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32nd Telecommunications Forum, TELFOR 2024

作者： Sladek, Predrag Faculty of Technical Sciences University of Novi Sad Computer Engineering and Computer Communications Department Novi Sad Serbia

ISBN: (纸本)9798350391053

This paper reviews three DSP-based audio virtualizer implementations used in immersive audio technologies, which have transformed sound experiences with limited speaker setups. Focusing on key parameters such as memory usage, processing power, and input/output layouts, the study employs multi-criteria decision-making (MCDM) approach to evaluate each virtualizer's strengths and trade-offs. The findings provide insights into the efficiency and feasibility of these virtualizers in delivering high-quality immersive audio in resource-constrained environments. © 2024 IEEE.

关键词： Decision making

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Lung Cancer Detection Using Machine Learning and Deep Learning Models 21

Lung Cancer Detection Using Machine Learning and Deep Learni...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alheeti, Khattab M. Ali Al-Shouka, Tabreer T. Majeed, Saleem Hamad Ahmed, Amjed Abbas College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq College of Computer and Information Technology University of Anbar Computer Sciences Department Anbar Iraq Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350374131

Technology and artificial intelligence play a significant role in improving healthcare and enable tasks to be automated. In addition, the diseases can be better understood and diagnosed faster, saving time and reducing costs. This study examines the impact of transfer learning models on the effectiveness of deep learning models in classifying lung cancer through the analysis of CT scan images. Additionally, it investigates the relative performance of various machine learning and deep learning models, encompassing Support Vector Machine (SVM) and convolutional neural net-works (CNN) such as InceptionV3, VGG16, Xception, ResNet50, and MobileNetV2, in the early detection of lung cancer based on CT scan images. The SVM model achieved an overall accuracy of 89% after preprocessing, the proposed approach was applied to five pre-trained models (ResNet50, In-ceptionV3, VGG16, Xception, MobileNetV2) using the dataset: Chest CT-Scan;Among the pre-trained CNN models, the Mo-bileNetV2 model achieved the highest accuracy of 98% and the lowest test loss, indicating it performed the best. The Xception model achieved the second-highest accuracy of 97%. The image pre-processing phase plays a significant role in im-proving system performance in terms of improving image contrast and increasing processing speed. © 2024 IEEE.

关键词： Support vector machines

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IoT eHealth Big Data Analytics Based on Machine Learning

IoT eHealth Big Data Analytics Based on Machine Learning

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Mahdi, Mohammed A. Ahamad, Shahanawaj Department of Information and Computer Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia

ISBN: (纸本)9789819965465

The convergence of health care and technology has led to the emergence of numerous inventive solutions, among which the Internet of Things (IoT) in tandem with Big Data Analytics and Machine Learning appears to hold significant potential. In the previous research that has been conducted on the Internet of Things (IoT), a variety of big data solutions has been presented for the purpose of evaluating massive amounts of data that have been obtained from a wide variety of sources in the field of smart health care. This evaluation is intended to take place to provide better care for the patients. This has been done with the goal of enhancing the quality of medical treatment offered to patients. Machine learning (ML) is a cutting-edge technology in the industry that has the capacity to analyze huge amounts of data and come to judgements based on those analyses in unique ways. This ability makes ML one of the most important developments in this field in recent years. In this article, we propose to use three distinct machine learning algorithms, each of which serves as a learning engine for big data analytics. Our goal is to investigate how quickly data is processed and how well it can be used to make predictions. Python is used throughout the whole training and testing process, including the simulation that is used to determine whether the model is effective. The findings of the simulation indicate that the suggested technique has a high degree of accuracy in determining the medical histories of patients based on the data provided by IoT devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Internet of things

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital Communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller Data security Cloud computing Cloud management

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Network Embedding Methods: Study and Comparison

EEA - Electrotehnica, Electronica, Automatica

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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 72-78页

作者： Benbatata, Sabrina Saoud, Bilal LIMPAF laboratory Department of Computer Sciences University of Bouira Bouira10000 Algeria Electrical Engineering Department Sciences and Applied Sciences Faculty LISEA laboratory University of Bouira Bouira10000 Algeria

Graph is a powerful language that can model many systems in different fields such as information sciences, social sciences, Biology, mathematics, physics, etc. Graphs can capture very well the relationships between nodes and their structure. Representing data through graphs has some limitations and is challenging to use them like input in machine learning and deep learning models. This challenge can be overcome by using network embedding. Embedding represents a network into low-dimensional vector space. Several methods have been proposed to embed networks. Methods like DeepWalk, Node2Vec and GraphSAGE have gained traction. DeepWalk employs random walks to capture local neighborhood information, generating embeddings using skip-gram models. Node2Vec extends this by incorporating biased random walks, balancing exploration and exploitation of the network. GraphSAGE adopts a neighborhood aggregation strategy, aggregating features from a node's local graph structure through different convolutional layers. These methods facilitate downstream tasks like node classification, link prediction and clustering. Challenges include scalability to large networks, handling heterogeneous information and preserving structural characteristics. Recent advancements integrate techniques like attention mechanisms and reinforcement learning for improved embeddings. Network embedding methods continue to evolve, catering to diverse applications in social networks, biology and recommendation systems, offering insights into complex network structures. In this paper we provide networks embedding concepts and some methods. A comparison between embedding methods is presented. Embedding network methods on node classification tasks on different benchmark datasets have been conducted in order to evaluate them. Through rigorous evaluation, we assess their effectiveness in capturing latent features and improving classification accuracy across various domains. © Editura ELECTRA 2024. All rights reserved

关键词： Graph embeddings

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions,and Future Directions

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computers, Materials & Continua 2024年第8期80卷 2511-2533页

作者： Ahmad Rahdari Ahmad Jalili Mehdi Esnaashari Mehdi Gheisari Alisa A.Vorobeva Zhaoxi Fang Panjun Sun Viktoriia M.Korzhuk Ilya Popov Zongda Wu Hamid Tahaei Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Engineering Gonbad Kavous UniversityGonbad-e Kavus49717-99151Iran Faculty of Computer Engineering K.N.Toosi University of TechnologyTehran196976-4499Iran Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Secure Information Technologies Department National Research University ITMOSt.Petersburg197101Russia School of Electrical and Computer Engineering Shiraz UniversityShiraz71946–84334Iran Department of Computer Science and Engineering Islamic Azad UniversityDamavand***Iran Department of R&D Shenzhen BKD Co.Ltd.Shenzhen518000China

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize ***-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human ***,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN *** findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy *** investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)***,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application ***,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.

关键词： Security privacy-preserving software-defined network internet of things

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Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

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computers, Materials & Continua 2024年第6期79卷 4725-4740页

作者： Djeldjli Halima Benatiallah Djelloul Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra Material Sciences Department Faculty of Material SciencesMathematics and Computer ScienceLaboratory of Sustainable Development and Computer Science(LSDCS)University of AdrarAdrar01000Algeria Department of Civil Engineering University of Sistan and BaluchestanZahedan98167-45845Iran Laboratory of Computer Production and Maintenance Engineering(LGIPM)University of LorraineChieulles57070France Laboratory of Energy Environment and Information System(LEEIS) University of AdrarAdrar01000Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first *** study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and *** proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN *** GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were *** model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

关键词： Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation

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Scalable dataset acquisition for data-driven lensless imaging 2

Scalable dataset acquisition for data-driven lensless imagin...

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Computational Optical Imaging and Artificial Intelligence in Biomedical sciences II 2025

作者： Hung, Clara S. Kabuli, Leyla A. Ponomarenko, Vasilisa Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

ISBN: (纸本)9781510684140

Data-driven developments in lensless imaging, such as machine learning-based reconstruction algorithms, require large datasets. In this work, we introduce a data acquisition pipeline that can capture from multiple lensless imaging systems in parallel, under the same imaging conditions, and paired with computational ground truth registration. We provide an open-access 25,000 image dataset with two lensless imagers, a reproducible hardware setup, and open-source camera synchronization code. Experimental datasets from our system can enable data-driven developments in lensless imaging, such as machine learning-based reconstruction algorithms and end-to-end system design. © 2025 SPIE.

关键词： Imaging systems

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