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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

IEEE Transactions on Mobile computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Generative AI in data Center Networking: Fundamentals, Perspectives, and Case Study

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IEEE Network 2025年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Wen, Yonggang Kim, Dong In Nanyang Technological University College of Computing and Data Science Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Guangdong University of Technology School of Automation China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Sungkyunkwan University College of Information and Communication Engineering Korea Republic of

Generative AI (GenAI), exemplified by Large Language Models (LLMs), such as OpenAI’s ChatGPT, is revolutionizing various fields. Central to this transformation is data Center Networking (DCN), which not only provides the infrastructure support for GenAI operation, but also provisions GenAI services to users. Hence, this article explores the interplay between GenAI and DCNs, analyzing their symbiotic relationship and mutual advances. We begin by reviewing the current challenges of DCNs and GenAI-based solutions, such as data augmentation, process automation, and domain transfer. We then discuss the distinctive characteristics of GenAI workloads on DCNs, gaining insights that catalyze the evolution of DCNs to more effectively support GenAI. Moreover, to illustrate the seamless integration of GenAI with DCNs, we present a case study on GenAI-empowered DCN digital twins. Specifically, we employ an LLM equipped with retrieval augmented generation to formulate optimization problems for DCNs (e.g., resource allocation and routing) and adopt diffusion-deep reinforcement learning to solve optimization. The experimental results on a representative DCN optimization problem, i.e., knowledge placement, demonstrate the validity and efficiency of our proposals. We anticipate that this article can promote further research to enhance the virtuous interaction between GenAI and DCNs. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Domain-adaptive state of health prediction of vehicle batteries powered by deep learning

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Cell Reports Physical science 2025年

作者： Li, Minghe Fei, Zicheng Yang, Luoxiao Zhang, Zijun Tsui, Kwok-Leung Department of Data Science College of Computing City University of Hong Kong Hong Kong Hong Kong School of Future Science and Engineering Soochow University Suzhou China College of Automation and Information Engineering Xi'an University of Technology Xi'an China Department of Industrial Manufacturing and Systems Engineering at the University of Texas-Arlington Arlington TX United States

State of health (SOH) estimation of battery packs in electric vehicles (EVs) is essential for transportation electrification safety and reliability. The noise and complexity of EV battery pack data hinder the effectiveness of various data-driven SOH estimation methods using lab data. To address these challenges and achieve more effective data-driven EV battery pack SOH predictions, this study develops a comprehensive deep-learning-based SOH modeling framework for EV batteries. The framework begins with a two-stage mode decomposition (TSMD) method designed to effectively identify neat SOH degradation patterns better representing noisy field data. Next, an endogenous and exogenous multibranch network structure with a hierarchically fused attention mechanism (EEMB-HiFA) is developed for real-time prediction of EV battery pack SOH. Computational experiments leveraging datasets from seven EVs are conducted to validate the accuracy and adaptiveness of the proposed EEMB-HiFA. The results show that the EEMB-HiFA can achieve a 96.49% improvement in accuracy compared to strong benchmarks considered. © 2025 The Author(s)

关键词： data feature fusion data-driven model deep learning domain adaptation electric vehicles lithium-ion battery signal denoising state of health

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

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IEEE Network 2025年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab Nanyang Technological University College of Computing and Data Science Singapore Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

The rapid development of generative artificial intelligence (AI) technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Enhancing lung cancer detection through integrated deep learning and transformer models

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Scientific Reports 2025年第1期15卷 1-28页

作者： Durgam, Revathi Panduri, Bharathi Balaji, V. Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Data Science AVN Institute of Engineering and Technology Hyderabad India Department of Information Technology Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad India Department of CSE-AIML Vardhaman College of Engineering Hyderabad India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering Kebri Dehar University Kebri Dehar Ethiopia Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia

Lung cancer has been stated as one of the prevalent killers of cancer up to this present time and this clearly underlines the rationale for early diagnosis to enhance life expectancy of patients afflicted with the condition. The reasons behind the usage of the transformer and deep learning classifiers for the detection of lung cancer include accuracy, robustness along with the capability to handle and evaluate large data sets and much more. Such models can be more complex and can help to utilize multiple modalities of data to give extensive information that will be critical in ascertaining the right diagnosis at the right time. However, the existing works encounter several limitations including reliance on large annotated data, overfitting, high computation complexity, and interpretability. Third, the issue of the stability of these models’ performance when applied to actual clinical datasets is still an open question;this is an even bigger issue that will greatly reduce the actual utilization of these models in clinical practice. To tackle these, we develop a novel Cancer Nexus Synergy (CanNS), which applies of A. Swin-Transformer UNet (SwiNet) Model for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) for fine-tuning parameters. This paper breaks new ground in that the presented elements are incorporated in a manner that co-operatively elevates the diagnostic capabilities while at the same time being computationally light and resilient. These are SwiNet for segmented analysis, XLG CancerNet for precise classification of the cases, and DevLO that optimizes the parameters of the lung cancer detection system, making the system more sensible and efficient. The performance outcomes indicate that the CanNS framework enhances the detection’s accuracy, sensitivity, and specificity compared to the previous approaches. © The Author(s) 2025.

关键词： And classification Deep learning Disease detection Lung Cancer Optimization Segmentation Transformer models

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Scalable Blockchain Technology for Tracking the Provenance of the Agri-Food

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Computers, Materials & Continua 2023年第5期75卷 3339-3358页

作者： B.Subashini D.Hemavathi Department of Data Science and Business Systems School of ComputingSRM ISTKattankulathurTamilnadu603203India

Due to an increase in agricultural mislabeling and careless handling of non-perishable foods in recent years,consumers have been calling for the food sector to be more *** to information dispersion between divisions and the propensity to record inaccurate data,current traceability solutions typically fail to provide reliable farm-to-fork histories of *** threemost enticing characteristics of blockchain technology are openness,integrity,and traceability,which make it a potentially crucial tool for guaranteeing the integrity and correctness of *** this paper,we suggest a permissioned blockchain system run by organizations,such as regulatory bodies,to promote the origin-tracking of shelf-stable agricultural *** propose a four-tiered architecture,parallel side chains,Zero-Knowledge Proofs(ZKPs),and Interplanetary File systems(IPFS).These ensure that information about where an item came from is shared,those commercial competitors cannot get to it,those big storage problems are handled,and the system can be scaled to handle many transactions at *** solution maintains the confidentiality of all transaction flows when provenance data is queried utilizing smart contracts and a consumer-grade reliance *** simulation testing using Ethereum Rinkeby and Polygon demonstrates reduced execution time,latency,and throughput overheads.

关键词： Blockchain IPFS sidechain supply chain management traceability

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Survey on age-related macular degeneration detection in OCT image

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International Journal of Computers and Applications 2024年第2期46卷 101-112页

作者： Deepti, S. Renu Karthik, P.C. Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology Kattankulathur India

Age-related Macular Degeneration (AMD) is the most common eye disease that causes visual impairment in elder people. Prevalently, AMD is detected by Spectral Domain Optical Coherence Tomography (SD-OCT) for diagnosis and monitoring purposes. In this work, 50 papers are reviewed to examine various approaches and their performance metrics. This paper reviewed various existing works related to AMD detection using different imaging modalities for quality images with high resolution. The investigation of case studies is analyzed on the basis of data collected from various patients undergoing several treatments and scans obtained from various imaging modalities. Furthermore, the analysis extends to the works that used different AI techniques, such as Deep Learning (DL), Machine Learning (ML), and Unsupervised Framework, for the detection of AMD-related diseases. Also, the analysis of different performance measures is done that shows the variations in terms of performance metrics such as accuracy, sensitivity, specificity, and so on. The outcome of this deep analysis shows that the system with the DL model reaches its utmost performance in AMD detection. Finally, gaps and challenges that are still lagging with this research area are summarized, which motivates the researchers to take away the work in an effective way. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diagnosis

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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High efficient net gain and low noise figure based vertical cavity semiconductor optical amplifiers for wavelength division multiplexing applications

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Journal of Optical Communications 2025年第2期46卷 247-256页

作者： Prabu, Ramachandran Thandaiah Balasubramanian, Arunsundar Balakrishnan, Nithyasundari Jebanazer, Jeneetha Kandasamy, Mohana Sundaram Govindaswamy, Nishanthi Mahmoud, Rashida Maher Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Tamilnadu Chennai India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai93104 India Department of ECE S.A. Engineering College Tamilnadu Chennai India Department of ECE Panimalar Engineering College Tamilnadu Chennai India Department of CSE R.M.D. Engineering College Kavarapettai Tamilnadu Chennai India Department of CSE R.M.K. Engineering College Kavarapettai Tamilnadu Chennai India Sohag Institute of Technology Sohag Egypt

This paper has demonstrated the high efficient net gain and low noise figure based vertical cavity semiconductor light amplifiers for wavelength division multiplexing applications. Previous study on the chip reflective gain variations versus SOA current under temperature effects is clarified. We have transferred light semiconductor amplifiers for wavelength multiplexing schemes applications. Amplifier output power is demonstrated with bias current and amplifier active layer region length variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The amplifier output noise variations are clarified against the bias current and amplifier active layer region width variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The output OSNR variations are studied clearly and deeply against the bias current and amplifier active layer region length variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The signal loss is demonstrated versus both active layer width/length and temperature based optimum 30 % gallium core dopant ratio and 28 % arsenide cladding dopant ratio at optimum input signal power of 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. SOA amplifier output power can be enhance with the management of both bias current and input signal power and the reduction of active amplifier length. The amplifier output noise can be enhance with the management of both bias current and input signal power and the reduction of active amplifier width. Output OSNR system can be improved with the management of both bias current and input signal power and the reduction of active amplifier length. The SOA amplifier gain can be improved with the management of both bias current and input signal power and the reduction of active amplifier length. The SOA amplifie

关键词： Noise figure

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