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Energy-efficient resource allocation in blockchain-based Cybertwin-driven 6G

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 103-114页

作者： Salim, Mikail Mohammed Pan, Yi Park, Jong Hyuk Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of Department of Computer Science Georgia State University Atlanta United States

Inefficient task scheduling schemes compromise network performance and increase latency for delay intolerant tasks. Cybertwin based 6G services support data logging of operational queries for appropriate resource allocation in Multi-Access Edge and Cloud Computing. In this paper, a Cybertwin assisted task scheduling scheme is proposed for energy-efficient job allocation to Edge-Cloud based on delay tolerance. Cybertwins using the Logger Function and the Digital Asset Function exchange Digital Assets with cloud operators using Smart Contracts to ensure maximum computational resources. The novelty of this study resides in using Communication Assistant Cybertwins to build profiles for all network entities and assign tasks based on their delay tolerance and the available resources of each Edge Datacenter and Cloud service provider. Compared with the Full Offloading scheme, experimental results demonstrate the higher energy efficiency of the task scheduling scheme. Offloading tasks to multiple data centers and smart contract-based selection of cloud operators result in reduced computation time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Smart contract

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Blockchain enabled MediVault for healthcare system

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Multimedia Tools and Applications 2025年第15期84卷 14805-14825页

作者： Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

The management of healthcare data has significantly benefited from the use of cloud-assisted MediVault for healthcare systems, which can offer patients efficient and convenient digital storage services for storing their medical records. Nevertheless, there are security risks associated with the current digital healthcare data, as malicious parties may work with cloud storage service providers to alter patient records or to directly disclose health record content to other adversaries for monetary advantage. In this paper, a blockchain-enabled MediVault for healthcare systems is proposed not only to provide safe storage of healthcare data in digital form but also secure access for authenticated entities such as patients, doctors, and pharmacists. In this MediVault, we introduced NFT generation and storage over the cloud using Interplanetary File System (IPFS) and FireBase as per user adaptability, Ehtereum Blockchain for immutability, and encryption and decryption using asymmetric keys for confidentiality. The empirical results and the performance evaluation demonstrate that the proposed MediVault is secure, simple, and efficient with a limited computation overhead. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Computing Performance Analysis of IEC 61499 Distributed Automation Systems 26

Computing Performance Analysis of IEC 61499 Distributed Auto...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Lyu, Tuojian Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331521950

This work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. These results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems. © 2025 IEEE.

关键词： Parallel processing systems

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Multi-source ECG Signal-Based Cardiac-Abnormality Pattern Classification with Fast–Slow Learning DNNs

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Journal of The Institution of Engineers (India): Series B 2025年 1-10页

作者： Samant, Sandhya Baloni, Dev Department of Computer Science and Engineering Quantum University Uttarakhand Roorkee247667 India

The classification of cardiac-abnormality patterns with ECG data plays a crucial role in the diagnosis as well as treatment and prognosis of diseases related to the human heart. With the advent of deep learning techniques, particularly convolutional, recurrent, and generative neural networks, there has been a significant advancement in the accuracy and efficiency of cardiac-abnormality pattern classification with electrocardiogram (ECG) data. However, with the availability of multitudes of freely available multi-source ECG data today, more attempts are required to develop new models that can handle and perform well on these datasets simultaneously. In this study, an attempt is made to develop a novel deep learning classification model with multi-source ECG dataset for cardiac abnormality pattern classification. The model uses the power of Transformer networks in their ability to produce low inductive bias towards learning representations and the power of Recurrence networks to memorize a compressed representation of a sequence is that it is beneficial for generalization. The transformers are the fast-stream component due to their sensitivity to sensory input and the RNs are slow-stream component due to their long-term memory sustenance. The multi-source ECG dataset is composed of 4 different and popular 12-lead ECG datasets available publicly for research purposes. The proposed model performed satisfactorily overall on a 27-class classification scenario. © The Institution of Engineers (India) 2025.

关键词： Electrocardiography

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How Lightweight Deep Learning Enhances Performance in DPU-Accelerated Autonomous Driving on Zynq SoC 11

How Lightweight Deep Learning Enhances Performance in DPU-Ac...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Hwang, Gyu Hyeon Oh, HoBin Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

This study presents a lightweight deep learning model developed for DPU-accelerated systems. It aims to provide real-time autonomous driving on resource-constrained systems such as the Ultra96v2. A customized kids electric car served as the platform. Custom power supply and steering control systems were set up in the car to enable real-world testing. To enhance inference performance, various methods were used. These included input size reduction, channel-pruning, and quantization. As a consequence, the pruned and quantized YOLOv3-Tiny model produced a frame rate of 67.592 FPS. This is roughly a 25x increase over the original YOLOv3's 2.715 FPS on Ultra96v2's PL domain. These results show that real-time deployment is feasible on FPGA-based platforms. The work offers insights for creating efficient and scalable embedded systems for self-driving vehicle system. © 2025 IEEE.

关键词： Direct digital control systems

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Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

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Multimedia Tools and Applications 2025年第10期84卷 7443-7475页

作者： Golande, Avinash L. Pavankumar, T. Department of Computer Science & Engineering KLEF A. P. Vijayawada India

Several approaches can detect early heart problems. Electrocardiograms (ECGs) are better and more affordable for early heart disease prediction. ECG data can better predict heart diseases and abnormalities. Standard methods are slow and limited to a narrow ECG signal pattern range. Newer deep learning-based algorithms require tedious and unimportant human feature extraction from raw ECG data. This study presents a new electrocardiogram-based cardiac disease detection system using an ensemble method for feature extraction and deep learning for classification. The approach includes pre-processing of input ECG signals, hybrid feature engineering, and disease categorization. Pre-processing the input signal preserved its integrity by reducing the artifact and noise using a robust filtering technique. ECG feature extraction should use generic automation and beat-specific features. We designed the hybrid feature engineering approach using a Convolutional Neural Network (CNN) and Stationary Wavelet Transform (SWT). The SWT method employs dynamic thresholding to segment ECG data into individual heartbeats, from which the beat-specific feature vector is derived. To automate the process of classifying cardiac diseases, a powerful 3-layer CNN has been trained to do so and now extracts those attributes automatically. Manifold learning of feature selection is then used to normalize the combined features from the two methods. Long Term Short Memory (LSTM), a deep learning classifier, has been given the ensemble feature vector. For LSTM, the sequential characteristics vector transmits information to the LSTM layer and then calculates the probabilities for each class to provide precise disease classification in the classification phase. The suggested model surpasses cutting-edge deep learning-driven techniques when used with a publicly available multi-disease ECG dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electrocardiograms

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Active and Reactive Power Flow Control of the Dual Active Bridge Converter 14

Active and Reactive Power Flow Control of the Dual Active Br...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Morris, Lauryn Francois, Thomas W. Saelens, Jonathan Oboreh-Snapps, Oroghene Fernandes, Arnold Uddarraju, Praneeth Strathman, Sophia A. Kimball, Jonathan W. Missouri University of Science and Technology Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331516116

The Dual Active Bridge (DAB) is a reliable and efficient converter capable of providing bi-directional power transfer and galvanic isolation. An ac-ac DAB can control both active and reactive power flow. The present work introduces a combined feedback/feed-forward current control system, utilizing the calculated and measured converter currents translated into the dq reference frame, to control the output power. The system was simulated in PLECS to demonstrate the control algorithm's ability to track the dq currents and provide the necessary output power. © 2025 IEEE.

关键词： Power electronics

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Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images

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Recent Advances in computer science and Communications 2024年第3期17卷 20-31页

作者： Srivastava, Somya Verma, Seema Das, Nripendra Narayan Sharma, Shraddha Dubey, Gaurav Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science and Engineering Guru Gobind Singh Indraprastha University Delhi India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science Pandit Deendayal Energy University Gujarat India Department of Computer Science KIET Group of Institutions Ghaziabad India

Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays. Objective: A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study. Methods: In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation. Results: The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%. Conclusion: The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models. © 2024 Bentham science Publishers.

关键词： Deep learning

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Physical-layer security for primary users in 5G underlay cognitive radio system via artificial-noise-aided by secondary users

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International Journal of computers and Applications 2024年第9期46卷 702-714页

作者： Al-Rjoob, Ayat M. Ababnah, Ahmad A. Al-Mistarihi, Mamoun F. Darabkh, Khalid A. Electrical and Communication Engineering Department German-Jordanian University and Technology Amman Jordan Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Computer Engineering Department School of Engineering The University of Jordan Amman Jordan

The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Security (PLS). Ensuring data confidentiality and integrity during wireless communications poses a primary challenge in IoT environments. Additionally, within the constrained frequency bands available, Cognitive Radio Networks (CRNs) has emerged as an urgent necessity to optimize spectrum utilization. This technology enables intelligent management of radio frequencies, enhancing network efficiency and adaptability to dynamic environmental changes. In this research, we focus on examining the PLS for the primary channel within the underlying CRNs. Our proposed model involves a primary source-destination pair and a secondary transmitter-receiver pair sharing the same frequency band simultaneously. In the presence of a common eavesdropper, the primary concern lies in securing the primary link communication. The secondary user (SU) acts as cooperative jamming, strategically allocating a portion of its transmission power to transmit artificial interference, thus confusing the eavesdropper and protecting the primary user's (PU) communication. The transmit power of the SU is regulated by the maximum interference power tolerated by the primary network's receiver. To evaluate the effectiveness of our proposed protocol, we develop closed-form mathematical expressions for intercept probability ((Formula presented.)) and outage probability (OP) along the primary communication link. Additionally, we derive mathematical expressions for OP along the secondary communications network. Furthermore, we investigate the impact of transmit power allocation on intercept and outage probabilities across various links. Through both simulation and theoretical analysis, our protocol aims to enhance protection and outage efficiency for the primary link while ensuring appropriate secondary

关键词： Cognitive radio

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Reconstruction Attacks in Template-Based ECG Biometric Recognition Systems

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IEEE Internet of Things Journal 2024年第8期11卷 14971-14984页

作者： Lee, Song-Hong Nien, Cing-Ping Wu, Shun-Chi Swindlehurst, A. Lee National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States

The success of Internet of Things (IoT) services will be determined by how the security of the IoT devices and the networks to which they are connected can be guaranteed. Integrating biometric recognition technology into IoT systems has gained popularity as a means of accomplishing this objective. Electrocardiograms (ECGs) have become a promising biometric tool for security because their intrinsic and dynamic nature makes them difficult to steal and forge for replay attacks. However, as with other biometric-based security approaches, attacks on ECG biometric systems have been developed. This study examines the vulnerability of template-based ECG biometric systems to reconstruction attacks. These attacks involve exposing the biometric templates of registered subjects in a hacked database and attempting to reconstruct their ECGs to spoof the system. Both deep learning models and 'heuristic' approaches are used to perform this task, depending on the intruder's level of knowledge of the template construction. Several reconstruction attack strategies are proposed and evaluated for fiducial- and PCA-based systems using ECGs from the Physikalisch-Technische Bundesanstalt database of 285 subjects. The experimental results demonstrate a reconstruction similarity of at least 0.93 and an increase in the false-positive identification-error rate of more than 73%. © 2014 IEEE.

关键词： Electrocardiograms

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