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Blockchain Integration in IoT:Applications,Opportunities,and Challenges

computers, Materials & Continua 2025年第5期83卷 1561-1605页

作者： Mozhgan Gholami Ali Ghaffari Nahideh derakhshanfard Nadir iBrAHIMO GLU Ali Asghar Pourhaji Kazem Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabriz***Iran Department of Computer Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbul34396Türkiye Department of Computer Science Khazar UniversityBakuAZ1096Azerbaijan MSDC Department Huawei R&D Center˙Istanbul34768Türkiye

The Internet has been enhanced recently by blockchain and Internet of Things(IoT)*** Internet of Things is a network of various sensor-equipped *** gradually integrates the Internet,sensors,and cloud *** is based on encryption algorithms,which are shared database technologies on the *** technology has grown significantly because of its features,such as flexibility,support for integration,anonymity,decentralization,and independent *** nodes in the blockchain network are used to verify online ***,this integration creates scalability,interoperability,and security *** the last decade,several advancements in blockchain technology have drawn attention fromresearch communities and *** technology helps IoT networks become more reliable and enhance security and *** also removes single points of failure and lowers the *** recent years,there has been an increasing amount of literature on IoT and blockchain technology *** paper extensively examines the current state of blockchain technologies,focusing specifically on their integration into the Internet of ***,it highlights the benefits,drawbacks,and opportunities of recent studies on security issues based on blockchain solutions into *** survey examined various research papers fromdifferent types of ***,a review of the other IoT applications has been included,focusing on the security requirements and challenges in IoT-based *** research directions are gathered for the effective integration of Blockchain and IoT.

关键词： Internet ofThings blockchain cybersecurity privacy security

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InGaZnO4 and In2O3 Mobility Trend: The role of Structural disorder from Amorphous to Crystalline States

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ACS Materials Letters 2025年 2024-2030页

作者： Jang, Hyeongjun Kim, Taehyun Lee, Wonsok Cho, Minhee Ha, daewon Alam, Muhammad A. Ye, Peide d. Jeong, Changwook Graduate School of Semiconductor Materials and Devices Engineering Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Advanced Device Research Lab Semiconductor R&D Center Samsung Electronics Suwon16677 Korea Republic of School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

This study investigates the mobility trends of indium-gallium-zinc oxide (IGZO) thin-film transistors over a wide range of fabrication conditions, from amorphous to crystalline phases. By utilizing machine learning potential (MLP) to generate 70 distinct IGZO structures, we analyze electron mobility while taking into account structural disorder and electron scattering mechanisms. Our findings reveal that mobility peaks at the transition from amorphous to nanocrystalline phases, and then, as more ordered, crystalline structures emerge, the mobility decreases sharply due to the distortion of polyhedron of indium-oxygen in well-ordered regions. These results offer critical insights into optimizing the fabrication conditions for high-performance IGZO devices by identifying the ideal structural phase for achieving maximum mobility. © 2025 American Chemical Society.

关键词： Electron scattering

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Accelerating deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seongwook Byeon, Gwangeun Lee, Seungeui Bae, Youngseok Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Advanced R&D Center Hanwha Systems Co. Ltd. Korea Republic of Agency for Defense Development Emerging Science and Technology Directorate Korea Republic of

ISBN: (纸本)9798331510756

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-delta-Immediate Compression (BdI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement, © 2025 IEEE.

关键词： data handling

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Energy-Efficient Cloud Computing Through reinforcement Learning-Based Workload Scheduling

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International Journal of Advanced computer science and Applications 2025年第4期16卷 645-656页

作者： Malipatil, Ashwini r Paramasivam, M.E. Gulyamova, dilfuza Saravanan, Aanandha ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Ghodhbani, refka Department of Computer Science and Engineering BNM Institute of Technology Bangalore India Department of ECE Sona College of Technology Salem India Computer Engineering Department University of Information Technologies in Tashkent Tashkent Uzbekistan Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (drL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes deep Q-Networks (dQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as round robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures. © (2025), (science and Information Organization). All rights reserved.

关键词： deep reinforcement learning

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Application of recurrent Neural Networks for Predictive Modeling of Electrical Submersible Pumps in Oil Extraction 11

Application of Recurrent Neural Networks for Predictive Mode...

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11th Vienna International Conference on Mathematical Modelling, MATHMOd 2025

作者： rebello, Carine Menezes Costa, Erbet Almeida Luiz de Abreu, Odilon Santana reges, Galdir Viena, Vinicius Guimares Mendes, Tefilo Paiva Fontana, Marcio ribeiro Bacellar, Francisco raphael ribeiro, Marcos Pellegrini Foresti, Bernardo Pereira Schnitman, Leizer Nogueira, Idelfonso B.r. Department of Chemical Engineering Norwegian University of Science and Technology Norway Department of Chemical Engineering Federal University of Bahia Bahia Brazil Department of Electrical and Computer Engineering Federal University of Bahia Bahia Brazil CENPES Petrobras R&D Center Ilha do Fundo Rio de Janeiro Brazil

This study presents a mathematical modeling framework that leverages recurrent neural networks (rNNs), specifically echo state networks (ESNs) and long short-Term memory (LSTM) architectures, for the predictive modeling of electrical submersible pumps (ESPs) in offshore oil extraction operations. Utilizing real operational data from an offshore oil field, the research addresses the inherent complexity and nonlinear dynamics of ESP systems by employing these recurrent structures to capture and represent the temporal dependencies within the data. Key challenges, such as data noise, variability, and limited diversity, are systematically tackled to ensure robust dynamic modeling. A comparative analysis evaluates the performance of ESN and LSTM models under these constrained data conditions, aiming to identify the superior model in terms of predictive accuracy and resilience. The modeling approach emphasizes the formulation, parameterization, and validation processes essential for effective ESP optimization and control in real-world industrial settings. Findings reveal the distinct strengths and limitations of each rNN variant when applied to offshore operational data. Copyright © 2025 The Authors.

关键词： Offshore oil well production

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Prodehaze: Prompting diffusion Models Toward Faithful Image dehazing

arXiv

引用

arXiv 2025年

作者： Zhou, Tianwen Wang, Jing Wu, Songtao Xu, Kuanhong Department of Computer Science University College London London United Kingdom R&D Center Beijing Lab Sony China Ltd Beijing China

recent approaches using large-scale pretrained diffusion models for image dehazing improve perceptual quality but often suffer from hallucination issues, producing unfaithful dehazed image to the original one. To mitigate this, we propose Prodehaze, a framework that employs internal image priors to direct external priors encoded in pretrained models. We introduce two types of selective internal priors that prompt the model to concentrate on critical image areas: a Structure-Prompted restorer in the latent space that emphasizes structure-rich regions, and a Haze-Aware Self-Correcting refiner in the decoding process to align distributions between clearer input regions and the output. Extensive experiments on real-world datasets demonstrate that Prodehaze achieves high-fidelity results in image dehazing, particularly in reducing color shifts. Our code is at https://***/TianwenZhou/Prodehaze. Copyright © 2025, The Authors. All rights reserved.

关键词： restoration

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Modified Kelvin-Voigt fractional derivative model for viscoelasticity measurement in optical coherence elastography

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Chinese Optics Letters 2025年第1期23卷 93-101页

作者： Chenming Yang Zhongliang Li Nan Nan Teng Liu Yaoli Luo Xiangzhao Wang Advanced Light Source and System R&D Center Department of Advanced Optical and Microelectronic EquipmentShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Optical coherence elastography(OCE)can quantitatively obtain the viscoelasticity of tissues using rheological models and is widely applied to the clinical diagnosis of ***,commonly used rheological models in OCE do not account for the distinctive dependence of high-frequency storage and loss moduli on frequency in tissues,which results in the rheological models failing to accurately measure the viscoelastic properties of *** this paper,a modified Kelvin-Voigt fractional derivative model is presented based on the power-law behavior of soft tissues and the dependence of high-frequency complex shear modulus on frequency in living *** the rheometer and OCE tests,the modified model can provide the prediction of the power-law relationship between the low-frequency shear viscosity and frequency;compared with the Kelvin-Voigt and Kelvin-Voigt fractional derivative models,the modified model has a higher goodness-of-fit(accuracy>96%)for the high-frequency storage moduli of gelatin ***,the proposed model can reduce the root mean square error of fit by approximately 83%for the high-frequency(1–128 kHz)storage modulus of the polydimethylsiloxane phantoms obtained from publicly available ***,the modified model accurately predicts the mechanical properties of biomimetic materials over a wide frequency range,with the potential to more accurately reflect pathological changes in tissues.

关键词： optical coherence tomography optical coherence elastography viscoelasticity rheological model

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An Intelligent diagnostic Approach for Early Alzheimer's diagnosis Based on Brain Area Screening and Self-Attention 25

An Intelligent Diagnostic Approach for Early Alzheimer's Dia...

引用

Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing

作者： Jinjin Li Jianzhi Man Qi Li School of Computer Science and Technology Changchun University of Science and Technology Changchun Jilin China The Suzhou R&D Center of the No 214 Institute of China North Industries Group Suzhou Jiangsu China

ISBN: (纸本)9798400712203

Aiming at the current Alzheimer's early diagnosis method has the problems of information redundancy and low precision. A brain region screening and self-attention based Alzheimer early intelligent diagnosis method is proposed. Firstly, a residual block-based fully convolutional neural network (res-FCNN) generative model is built for MrI image pre-training. Subsequently, the first layer feature maps of the res-FCNN model are read for brain region ranking to screen out some brain regions with lower weights. Then, a neural network (SPANN) classification model based on spatial position encoding and self-attention mechanism was constructed, which was built based on the results of brain region screening, and at the same time, coding and decoding the brain regions based on the self-attention mechanism was performed. The experimental results show that the proposed brain region screening and self-attention based Alzheimer's early intelligent diagnosis method effectively eliminates the redundant information of MrI images and achieves a classification accuracy of 98.7%. The proposed method provides a new idea for the early diagnosis of Alzheimer.

关键词： Alzheimer's

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Network View and Cognitive Mechanism for Virtual Network resource Management Based Intelligent

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Chinese Journal of Electronics 2025年第3期23卷 574-578页

作者： Xiaochun Wu Chunming Wu Bin Wang Yaguan Qian Julong Lan Computer Science College Zhejiang University Hangzhou China Institute of Network and Communication Engineering Zhejiang Gongshang University Hangzhou China National Digital Switching System Engineering & Technology R&D Center Zhengzhou China

Network virtualization has been a particularly important driving force behind the development of NGN. Finding effective ways to control self-adapting virtual networks has become a research hot spot. This paper tries to find an efficient way in combining dynamic resource sensing with traffic character and requirements in virtual network, based on that an intelligent cognition based virtual network resources management model has been put forward. We design a network topology view by coloring network topology matrix with current resources model. We present an intelligent virtual resource management mechanism based on the proposed network topology view. Experiment results illustrate that the proposed mechanism can improve VN capacity by automatic resource adjust-ment, which can obtain high utilization of VN resources without increasing computational expenses.

关键词： Technological innovation Network topology Computational modeling Force dynamics Network resource management dynamic scheduling Sensors Virtualization Next generation networking

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Mathematical Modelling of dengue Transmission with Adaptive Fuzzy Backstepping Control and rK-5 Numerical Method

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International Journal of Applied and Computational Mathematics 2025年第3期11卷 1-29页

作者： Vijayalakshmi, G.M. Ariyanatchi, M. Akgül, Ali Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamilnadu Avadi 600062 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Mathematics Art and Science Faculty Siirt University Siirt 56100 Turkey Department of Computer Engineering Biruni University Istanbul Topkapı 34010 Turkey Department of Mathematics Mathematics Research Center Near East University Near East Boulevard Mersin 10 Nicosia 99138 Turkey

The aim of this research is to create an adaptive nonlinear fuzzy backstepping robust controller for a dengue disease outbreak model. We use an SIr-SI compartmental mathematical model to analyse the diffusion mechanism of dengue disease. A nonlinear adaptive controller regulates prevention strategies for susceptible human beings and enhances hospitalization-based treatment for the infected people. Besides, the spraying of chemical insecticides will reduce the infected mosquito population. Adaptation laws are used to derive fuzzy system parameters, enhancing the system's adaptability. Then we construct a suitable Lyapunov function, which guarantees convergence, robustness, and stability for a closed-loop system. Finally, we plot all the system's control variables graphically and calculate their numerical approximations using the runge–Kutta technique of fifth order. These simulations demonstrate that the proposed adaptive controllers can effectively control the dengue disease and ensure the stability of the system. Hence, we conclude that adaptive backstepping control measures can protect more people from dengue infection. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Adaptive back stepping dengue model Fuzzy system Lyapunov function runge–Kutta method

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