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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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FedCSpc: A Cross-Silo Federated Learning System with Error-Bounded Lossy Parameter Compression

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IEEE Transactions on Parallel and distributed Systems 2025年第7期36卷 1372-1386页

作者： Zhang, Zhaorui di, Sheng Zhao, Kai Jin, Sian Tao, dingwen Ji, Zhuoran Liu, Benben Alharthi, Khalid Ayed Cao, Jiannong Cappello, Franck The Hong Kong Polytechnic University Department of Computing Hong Kong Florida State University Department of Computer Science United States Temple University Department of Computer and Information Sciences United States Indiana University Bloomington Luddy School of Informatics Computing and Engineering United States Shandong University School of Cyber Science and Technology China The University of Hong Kong LSCM R&D Center Hong Kong University of Bisha Department of Computer Science College Of Computing And Information Technology Saudi Arabia Argonne National Laboratory United States

Cross-Silo federated learning is widely used for scaling deep neural network (dNN) training over data silos from different locations worldwide while guaranteeing data privacy. Communication has been identified as the main bottleneck when training large-scale models due to large-volume model parameters and gradient transmission across public networks with limited bandwidth. Most previous works focus on gradient compression, while limited work tries to compress parameters that can not be ignored and extremely affect communication performance during the training. To bridge this gap, we propose FedCSpc: an efficient cross-silo federated learning system with an XAI-driven adaptive parameter compression strategy for large-scale model training. Our work substantially differs from existing gradient compression techniques due to the distinct data features of gradient and parameter. The key contributions of this paper are fourfold. (1) Our designed FedCSpc proposes to compress the parameter during the training using the state-of-the-art error-bounded lossy compressor – SZ3. (2) We develop an adaptive compression error bound adjustment algorithm to guarantee the model accuracy effectively. (3) We exploit an efficient approach to utilize the idle CPU resources of clients to compress the parameters. (4) We perform a comprehensive evaluation with a wide range of models and benchmarks on a GPU cluster with 65 GPUs. results show that FedCSpc can achieve the same model accuracy as FedAvg while reducing the data volume of parameters and gradients in communication by up to 7.39× and 288×, respectively. With 32 clients on a 4Gb size model, FedCSpc significantly outperforms FedAvg in wall-clock time in the emulated WAN environment (at the bandwidth of 1 Gbps or lower without loss of generality). © 1990-2012 IEEE.

关键词： Bandwidth compression

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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...

引用

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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A Hardware-Friendly AVS3 Entropy decoder Architecture for 8K Ultra-High-definition Video

A Hardware-Friendly AVS3 Entropy Decoder Architecture for 8K...

引用

data Compression Conference (dCC)

作者： Jiaqi Zhang Xu Han Chuanmin Jia Shanshe Wang Siwei Ma National Engineering Research Center of Visual Technology School of Computer Science Peking University China Wangxuan Institute of Computer Technology Peking University China Information Technology R&D Innovation Center of Peking University China

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Logarithmic binary arithmetic coding (LBAC) is a entropy coding method first introduced in AVS2 and now used in the third generation of audio video coding standard (AVS3). While LBAC provides high coding efficiency, the data dependencies in AVS3 make it challenging to parallelize parsing syntax element. To enhance the throughput of AVS3 LBAC entropy decoding engine, a sophisticated hardware implementation architecture is meticulously designed in the paper. The proposed method decouples the bitstream decoding process into four distinct modules. To ensure the decoding efficiency and performance, an advanced parallel pipeline is carefully designed. Furthermore, we develop sub-branch state prediction mechanism and grouped decoding method to further improve the decoding efficiency. The proposed AVS3 entropy decoder has been implemented on the S10 FPGA System Board, and experimental results show that the proposed method could achieve real-time and seamless decoding efficiency for 8K AVS3 bitstreams, even at bitrates as high as 207.1Mbps.

关键词： Video coding Syntactics Streaming media Throughput Entropy real-time systems decoding Field programmable gate arrays Standards Binary sequences

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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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