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Optimal Resource Allocation in Fog Computing for Healthcare Applications

computers, Materials & Continua 2022年第6期71卷 6147-6163页

作者： Salman Khan Ibrar Ali Shah Nasser Tairan Habib Shah Muhammad Faisal Nadeem Department of Computer Software Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaK.S.A Informatics Complex H-8Islamabad44000Pakistan

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and ***,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT ***,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time ***,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of *** paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the *** proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load *** consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average *** proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been *** experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

关键词： Fog computing internet of health things resource management

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Blockchain-Assisted Fine-Grained Deduplication and Integrity Auditing for Outsourced Large-Scale Data in Cloud Storage

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IEEE Internet of Things Journal 2025年第12期12卷 21662-21678页

作者： Liu, Bingyun Zhang, Xiaojun Yang, Xingchun Zhang, Yuan Xue, Jingting Zhou, Rang Southwest Petroleum University School of Computer Science and Software Engineering Engineering Research Center for Intelligent Oil and Gas Exploration Chengdu610500 China Sichuan Police College Intelligent Policing Key Laboratory of SichuanProvince Luzhou646000 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China Chengdu University of Technology School of Computer Science and Cyber Security Chengdu610059 China

Cloud computing has emerged as a promising mode for storaging vast quantities of big data, which is vulnerable to potential security threats, making it urgent to ensure data confidentiality and integrity auditing. In addition, as a large number of duplicate data files exist in cloud storage, data deduplication is significant to improve storage efficiency. In this article, we propose a blockchain-assisted fine-grained deduplication and integrity auditing scheme for outsourced large-scale data in cloud storage, achieving internal deduplication and cross-user external deduplication for ciphertexts and authentication tags. By constructing sparse summation ciphertext tree, the scheme implements Proofs of Ownership protocol through vector commitment, and guarantees the retrieval of distributed deduplication data blocks by designing the reconstruction matrix and bloom filter. The scheme exploits blockchain and smart contracts to ensure transparent integrity auditing without a third-party auditor (TPA), thereby avoiding malicious auditing biases. Security analysis and performance evaluation demonstrate the feasibility of the scheme for deploying in cloud storage systems. © 2014 IEEE.

关键词： Data integrity

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Comparative Analysis of Differential Privacy Implementations on Synthetic Data 15

Comparative Analysis of Differential Privacy Implementations...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： Data integrity

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Improving Federated Learning Efficiency with PruneFL: A Healthcare Perspective 24

Improving Federated Learning Efficiency with PruneFL: A Heal...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Shahid, Alvina Arshad, Muhammad Muneeb Azam, Farooque Anwar, Muhammad Waseem Shafi, Imran Department of Computer and Software Engineering EME College National University of Sciences and Technology Punjab Islamabad Pakistan School of Interdisciplinary Engineering and Sciences National University of Sciences and Technology Punjab Islamabad Pakistan

ISBN: (纸本)9798400717482

In healthcare, accessing diverse and large datasets for machine learning poses challenges due to data privacy concerns. Federated learning (FL) addresses this by training models on decentralized data while preserving privacy. We propose PruneFL, a FL paradigm that leverages model pruning to reduce complexity and enhance efficiency. PruneFL strikes a balance between model accuracy and efficiency by reducing parameters, accelerating convergence, and minimizing communication overhead. Our approach addresses critical FL challenges and demonstrates efficacy in resource-constrained environments like healthcare. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Performance-Based Scheduling in Distributed Mixed Criticality Systems

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IEEE Access 2025年 13卷 95321-95340页

作者： Ali, Amjad Wasly, Saud Khattak, Asad Masood Ali, Ihsan Iqbal, Shahid Hayat, Bashir University of Swat Department of Computer and Software Technology Swat Khyber Pakhtunkhwa Saidu Sharif19130 Pakistan King Abdulaziz University Electrical and Computer Engineering Department Jeddah21589 Saudi Arabia Zayed University College of Technological Innovation Dubai United Arab Emirates Institute of Management Sciences Peshawar School of Computer Science and IT Peshawar25000 Pakistan

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores while considering interference, ensuring better performance and adherence to critical deadlines. This method reduces task waiting times, which minimizes the scheduling duration and improving the schedulability of the task set. The novel proposed approach is compared with Mixed-Criticality Multicore Compositional Earliest Deadline First (MMC-EDF), Global and Mixed Criticality Partitioning (MC-Partitioning) approaches to show the efficiency. The proposed approach schedules 75% tasks while the MMC-EDF, Global and MC-Partitioning approaches schedules 65%, 0% and 18% tasks respectively for target utilization U=0.8. As the utilization of mixed criticality (MC) workload increases, the schedulability of MC task sets decreases, but still the proposed approach performs better than the MMC-EDF, Global and MC-partitioning approaches. © 2013 IEEE.

关键词： Embedded systems

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Comparison of pulse rate variability from post-auricula and heart rate variability during different body states for healthy subjects

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Journal of Medical engineering and Technology 2023年第3期47卷 179-188页

作者： Qi, Yusheng Zhang, Aihua Ma, Yurun Chang, Tingting Xu, Jianwen College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of Technology Lanzhou China National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou University of Technology Lanzhou China College of Computer and Communication Lanzhou University of Technology Lanzhou China

Heart rate variability (HRV) extracted from the electrocardiogram (ECG) is an essential indicator for assessing the autonomic nervous system in clinical. Some scholars have studied the feasibility of pulse rate variability (PRV) instead of HRV. However, there is little qualitative research in different body states. In this paper, the photoplethysmography (PPG) of postauricular and finger and the ECG of fifteen subjects were synchronously collected for comparative analysis. The eleven experiments were designed according to the daily living state, including the stationary state, limb movement state, and facial movement state. The substitutability of nine variables was investigated in the time, frequency, and nonlinearity domain by Passing Bablok regression and Bland Altman analysis. The results showed that the PPG of the finger was destroyed in the limb movement state. There were six variables of postauricular PRV, which showed a positive linear relationship and good agreement (p > 0.05, ratio (Formula presented.) 0.2) with HRV in all experiments. Our study suggests that the postauricular PPG could retain the necessary information of the pulse signal under the limb movement state and facial movement state. Therefore, postauricular PPG could be a better substitute for HRV, daily PPG detection, and mobile health than finger PPG. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Photoplethysmography

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Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline 18th

Profinder: Towards Professionals Recognition on Mobile Devi...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. This paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Analysis of the External Characteristics and Eddy Current Loss of PMSG-Based WECS Relying on a Multi-Domain Coupling Model 19th

Analysis of the External Characteristics and Eddy Current Lo...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zuo, Peng Li, Weili Liu, Xiaoke Liu, Wenmao Li, Dan Qiao, Tianhuai School of Electrical Engineering Beijing Jiaotong University Beijing China Department of Electrical Engineering Tsinghua University Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9789819613861

With the trend towards larger-scale wind generators, the internal physical field and control strategy of high-capacity generator is becoming increasingly complex. The physical field change law inside the generator is often overlooked by the conventional wind energy conversion system (WECS) model. Therefore, developing a comprehensive and precise WECS model is essential for the safe operating of wind generators. Aiming at the above problems, this paper takes a 5 MW direct-drive permanent magnet synchronous generator (PMSG) as an example, and establishes a multi-domain coupling model. This approach can be used to investigate how the external characteristics of PMSG change under different operating conditions. Additionally, it has the ability to monitor how eddy currents are distributed in permanent magnets (PM), supporting generator design theoretically. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Deeply‐Recursive Attention Network for video steganography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1507-1523页

作者： Jiabao Cui Liangli Zheng Yunlong Yu Yining Lin Huajian Ni Xin Xu Zhongfei Zhang College of Computer Science and Technology Zhejiang UniversityHangzhouChina School of Software Technology Zhejiang UniversityNingboChina College of Information Science and Electronic Engineering Zhejiang UniversityHangzhouChina Shanghai SUPREMIND Technology Co. Ltd.ShanghaiChina College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Binghamton UniversityBinghamtonNew YorkUSA

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover *** is the first and foremost requirement of any steganographic *** by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is *** DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)***,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover *** NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret ***,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video *** results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

关键词： data privacy video processing

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