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IET Collaborative Intelligent Manufacturing 2025年第1期7卷

作者： Shah, Sajid Madni, Syed Hamid Hussain Hashim, Siti Zaitoon Mohd Faheem, Muhammad Shahzad, Hafiz Muhammad Faisal Faculty of Computing Universiti Teknologi Malaysia Johor Skudai Malaysia School of Computer Science University of Southampton Malaysia Johor Iskandar Puteri Malaysia School of Technology and Innovations University of Vaasa Vaasa Finland VTT Technical Research Centre of Finland Ltd. Espoo Finland Department of Computer Science University of Sargodha Sargodha Pakistan

The industrial Internet of Things (IIoT) is revolutionising manufacturing and production of small and medium enterprises (SMEs) by enhancing efficiency and product quality. While developed countries like the USA, UK, Canada, Finland, and Japan have widely adopted IIoT, developing nations such as Bangladesh, India, Pakistan, and Malaysia are still lagging. This study explores IIoT adoption in manufacturing SMEs, emphasising its potential for economic growth despite challenges like budget constraints and skill gaps in developing countries. It presents a novel model based on 17 factors from the TOEI (Technology, Organization, Environment, and Individual) framework to support decision-makers in integrating IIoT technologies. The model’s reliability and validity are confirmed through rigorous testing and a survey of three SMEs. This proposed model serves as a roadmap for SMEs, breaking down complex processes into manageable steps, and providing SMEs with a structured approach. © 2025 The Author(s). IET Collaborative Intelligent Manufacturing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Fabrication

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A Comprehensive Survey on GenAI-Enabled 6G: Technologies, Challenges, and Future Research Avenues

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年

作者： Sheraz, Muhammad Chuah, Teong Chee Tareen, Wajahat Ullah khan Al-Habashna, Ala'a Saeed, Sohail Imran Ahmed, Manzoor Lee, It Ee Guizani, Mohsen Multimedia University Faculty of Artificial Intelligence and Engineering Selangor Cyberjaya63100 Malaysia University of Jeddah College of Engineering Department of Electrical and Electronic Engineering Jeddah21589 Saudi Arabia Al Hussein Technical University School of Computing and Informatics Amman Jordan Bahria University Department of Computer Engineering Karachi Pakistan School of Computer Science and Information Engineering Hubei Engineering University Xiaogan432000 China Abu Dhabi United Arab Emirates

The integration of artificial intelligence (AI) in 6G demonstrates a transformative leap in redefining network efficiency, intelligence, and adaptability. However, AI largely leverages discriminative models relying on labelled and quality data, where data accessibility remains serious concern. Generative AI (GenAI) has gained traction due to its immense potential in resolving the issue of data scarcity, complexity, and incompleteness. GenAI models excel in understanding underlying data distributions, enabling them to generate synthetic data that mirrors real-world patterns. GenAI supports adaptive learning and scenario modeling, making it indispensable for addressing the unpredictability and complexity inherent in 6G networks. Since the complexity of wireless communication systems is increasing and the demand for such systems is growing, GenAI presents new ideas for enhancing network performance, increasing system efficiency, and developing intelligent decision-making capabilities. This survey paper investigates the promising role of GenAI in the evolution of 6G networks. An in-depth discussion of notable GenAI models is presented, outlining their application in enhancing key network components. Specifically, the application of GenAI in advanced technologies including reconfigurable intelligent surfaces (RIS), unmanned aerial vehicles (UAVs), digital twins (DTs), and integrated sensing and communications (ISACs) is thoroughly investigated with respect to optimize the adaptability, flexibility, and robustness of the wireless networks. Moreover, use cases of GenAI-enabled wireless networks are presented to highlight the realization of GenAI in 6G. The paper also presents the lessons learned, existing challenges, and future research directions. This paper systematically explores GenAI and its pivotal role in the development of 6G, providing a foundation for researchers to further investigate and advance GenAI-enabled 6G. © 2020 IEEE.

关键词： Deep learning

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

arXiv

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arXiv 2025年

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk Electrical and Computer Engineering Department The Ohio State University United States BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025, CC BY.

关键词： Deep reinforcement learning

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Early Detection of Chikungunya Virus Using Silver and Zinc Selenide Multilayer Structure Utilizing the Surface Plasmon Resonance: A Numerical Approach

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Plasmonics 2025年 1-12页

作者： Ansari, Gufranullah Oweis, Rami Baldaniya, Lalji Barakat, Muna S, Renuka Jyothi Kaur, Irwanjot Al-Hasnaawei, Shaker Maharana, Laxmidhar Chauhan, Ashish Singh Purohit, Bilakshan Uniyal, Arun Department of Electronics & Comm. Engg School of Engineering and Computing Dev Bhoomi Uttarakhand University Chakrata Road Uttarakhand Manduwala Naugaon 248007 India Modern College of Business and Science Muscat Oman Jordan University of Science and Technology Irbid 22110 Jordan Department of Pharmaceutical Sciences Faculty of Health Sciences Marwadi University Research Center Marwadi University Gujarat Rajkot India Faculty of Pharmacy Applied Science Private University Amman Jordan Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to Be University) Karnataka Bangalore India Department of Allied Healthcare and Sciences Vivekananda Global University Rajasthan Jaipur 303012 India College of Pharmacy the Islamic University Najaf Iraq Department of Medical Analysis Medical Laboratory Technique College the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Pharmaceutical Sciences Siksha’O’Anusandhan (Deemed to Be University) Odisha Bhubaneswar 751030 India Division of Research and Innovation Uttaranchal Institute of Pharmaceutical Sciences Uttaranchal University Uttarakhand Dehradun India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Center for Global Health Research Saveetha Medical College & Hospitals Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai 602105 India Depertment of Electronics and Communication Engineering Graphic Era Hill University Uttarakhand Dehradun 248002 India Department of Electronics and Communication Engineering Graphic Era (Deemed to Be University) Uttarakhand Dehradun 248002 India

The current work examines the theoretical aspects of a very sensitive surface plasmon resonance sensor for detecting the chikungunya virus. Silver and zinc selenide are the only material layers comprising the sensing structure. They are deposited one on top of the other on the surface of the BK7 glass prism. For best sensing performance, the geometric characteristics of each layer are tuned. The chikungunya virus detection capacity of the proposed sensor structure is demonstrated by its results. The obtained simulated results give a high sensitivity of 384.7 degree/RIU with a detection accuracy of 0.471 degree⁻¹, the figure of merit of 181.19 RIU⁻¹. These parameters support our assertion that we have designed a high-performance-based sensor to identify the chikungunya virus in the blood sample. The results are simulated using MATLAB programming based on the transfer matrix method (TMM), and they are then examined further to assess the sensor’s performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Chikungunya virus Figure of merit Sensitivity Surface plasmons Transfer matrix method

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

引用

IEEE Conference on Wireless Communications and Networking

作者： Sarah Al–Shareeda Fusun Ozguner Keith Redmill Trung Q. Duong Berk Canberk Electrical and Computer Engineering Department Center for Automotive Research (CAR) The Ohio State University USA BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

关键词： 6G mobile communication Performance evaluation Identity-based encryption Authentication Deep reinforcement learning Reproducibility of results Resource management Cryptography Optimization Software development management

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Reducing the clustering challenge in the IoT using two disjoint convex hulls

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Scientific Reports 2025年第1期15卷 1-10页

作者： Li, Huxiong Sadeghi Bigham, Bahram Gheisari, Mehdi Karamoozian, Aminreza Sun, Panjun Wan, Yi Institute of Artificial Intelligence Shaoxing University Zhejiang China Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran Department of computer science and Engineering Saveetha School of engineering Saveetha Institute of Medical and Technical Science Tamil Nadu India Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of R & D Shenzhen BKD Co LTD Shenzhen China Centre for Augmented Intelligence and Data Science School of Computing University of South Africa Johannesburg South Africa

Accurate clustering of IoT devices is a promising challenge. We have observed that a few studies have been performed to address this challenge. However, they are expensive or do not shape accurate clustering. To fill this gap, in this study, we first solve a geometric version of a big challenge in pure mathematics: the NP-hard “Almost ” problem. Then, we solve it in a polynomial time. To clarify the concept, we present it as the “Two Disjoint Convex Hulls” challenge. We solve this challenge using two algorithms: the first is “Naive” and the second is faster than the “Naive” one can solve it in polynomial order,. In addition to providing a mathematical proof of our solution, we demonstrate its superior performance within an IoT industrial ecosystem. © The Author(s) 2025.

关键词： Algorithm Binary sensor network Computational geometry Convex hull SAT Separation axis theorem

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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RFAE: A high-robust feature selector based on fractal autoencoder

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Expert Systems with Applications 2025年 285卷

作者： Ou, Jingfeng Li, Jiawei Xia, Zhiliang Dai, Shurui Guo, Yan Jiang, Limin Tang, Jijun College of Engineering Southern University of Science and Technology Shenzhen 518055 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518107 China Department of Public Health Sciences University of Miami Miami FL33136 United States Centre for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China

Feature selection aims to consistently identify an optimal subset of features that effectively represents the entire dataset or enhances performance in downstream tasks. While deep learning-based approaches have made significant progress in feature selection, they continue to face key challenges, including instability in selected features, limited receptive fields in feature-selection layers due to architectural constraints, and suboptimal utilization of available sample information. To address these limitations, we propose the Robust Fractal Autoencoder (RFAE), an enhanced variant of the Fractal Autoencoder (FAE) designed to improve feature selection stability and adaptability. RFAE introduces three critical advancements: 1) Novel utilization of weight exponentiation to rectify the concern of FAE selecting a reduced number of features than designated. 2) Adoption of a dynamic and tailored strategy to optimize feature selection weights during the training process. 3) Introduction of a optional classification module, facilitating extension to supervised feature selection scenarios. We systematically evaluate RFAE against 14 established feature selection methods. Our experiments span 14 publicly available benchmark datasets, a large-scale GEO gene expression dataset, and a synthetic dataset with known ground-truth features. The results demonstrate that RFAE consistently selects features that achieve lower reconstruction errors while ensuring higher stability across repeated experiments, highlighting its robustness and effectiveness in feature selection tasks. © 2025

关键词： Feature Selection

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Comparative analysis of regression models to predict the performance of the dual fuel engine operating on diesel and hydrogen gas

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International Journal of Hydrogen Energy 2025年

作者： S, Priya Feenita, C. Goel, Uday T, Manoranjitham Duraisamy, Boopathi Subramanian, Balaji Ganeshan, Kavitha Josephin JS, Femilda Albeshr, Mohammed F. Pugazhendhi, Arivalagan Varuvel, Edwin Geo Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Automobile Engineering College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Mechanical Engineering College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Science and Humanities Agni College of Technology Thazhambur Chennai600130 India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Zoology College of Sciences King Saud University P.O. Box. 2455 Riyadh11451 Saudi Arabia Research and Development Office Asia University Taichung City Taiwan Department of Mechanical Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Autoronics Institute of Automobile Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India

Internal combustion engines (ICEs) have long been essential in both the transportation and industrial sectors, providing primary power for vehicles, ships, and machines globally. Optimising the efficiency of ICEs is vital for decreasing their environmental impart, as increased fuel efficiency and lower emissions play a significant role in mitigating the effects of climate change as well as improving air quality. This study employed 15 regression algorithms and machine learning approaches to analyse and anticipate the performance parameters of ICEs that run on hydrogen-diesel in dual fuel mode. The input parameters include engine torque, speed, hydrogen flow rate, brake power and diesel energy share to hydrogen supply and the output parameters are brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and actual air intake. The model's performance is evaluated using five different performance metrics. Among the studied algorithms, the RANSAC Regressor demonstrated exceptional predictive capability, reaching an R-squared value of 0.999, a mean squared error (MSE) of 0.0064, a root mean square error (RMSE) of 0.08, and a mean absolute error (MAE) of 0.057. These outcomes show the algorithm's accuracy and precision in capturing the complicated data of engine system. The equivalency ratio, volumetric efficiency, brake thermal efficiency, brake specific fuel consumption, and actual air intake are among the critical performance outputs that are optimised by utilizing key input parameters like engine load, rotational speed, hydrogen flow rate, brake power, and the diesel fuel energy share. This study highlights the significant potential of machine learning in optimising ICE performance, offering a reliable alternative to traditional experimental analysis by reducing both risk and economic costs. The research findings also support the paradigm shift towards intelligent and sustainable energy systems by compellingly advocating for the inclusion of da

关键词： Air intakes

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Tools for ubiquitous PACS system 1

引用

4th International Conference on Human-Centric computing, HumanCom'11 and the 6th International Conference on Embedded and Multimedia computing, EMC'11

作者： Dragan, Dinu Ivetic, Dragan Faculty of Technical Science/Computing and Control Department University of Novi Sad Novi Sad Serbia

ISBN: (数字)9789400721050

ISBN: (纸本)9789400721043

Ubiquitous computing in healthcare (ubiquitous healthcare) is a very appealing and beneficial goal. It is a mean to achieve patient-centric healthcare and a mean to improve healthcare in general. PACS system is important part of a modern day hospital. To achieve ubiquitous healthcare, it is necessary to build ubiquitous PACS system. Thus a traditionally closed system based on large and location-dependent workstations will be accessible to all users regardless of their location and devices they use. This is achievable only by improving PACS system functionality without sacrificing its interoperability. © 2011 Springer science+Business Media B.V.

关键词： Scalability

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