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Optimization of 6G resource allocation using CyberTwin function-based service enhancement scheme

Eurasip Journal on Wireless Communications and Networking 2025年第1期2025卷 1-27页

作者： Aldrees, Asma Min, Hong Daradkeh, Yousef Ibrahim Dutta, Ashit Kumar Anjum, Mohd Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha61421 Saudi Arabia School of Computing Gachon University Seongnam13120 Korea Republic of Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Al-Kharj16273 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences Almaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Computer Engineering Aligarh Muslim University Aligarh202002 India

The most innovative Service-Optimized Logging for Resource Allocation (SOL-RA) is in situations that support 6G connection. The CyberTwin architecture optimizes the quality of service provided to end users in 6G communications by using the dependability of terahertz connections and interactions of a machine-type nature. Elastically sharing elastic resources among users is made possible by the proposed resource allocation strategy, which makes use of proprietary logger functions. This ensures that efficient allocation is achieved without overlapping or lengthy wait periods. Within the framework of SOL-RA, a categorization mechanism is implemented for dense requests, which differentiates them as either stationary or priority services. Resource allocations are dynamically done based on this categorization, which enhances the organization's responsiveness to different needs. Through a tree classifier learning mechanism, CyberTwin logs play an essential part in the processing of requests and the preparation of resources. This helps to ensure that resources are distributed without inefficiency. Requests that are generating a backlog are identified and processed in parallel by the system, which significantly reduces the amount of time that is wasted waiting. In order to guarantee a specialized distribution for the categorized outputs, resource allocations are directed by CyberTwin data logs. An examination of the performance of the SOL-RA scheme takes into account important metrics such service latency, service backlog, resource usage, and request-to-response ratio. This research offers insights into the efficacy of the scheme in maximizing service quality and resource consumption in 6G settings. © The Author(s) 2025.

关键词： Resource allocation

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A Lightweight Authentication Protocol for RFID-Assisted Supply Chain Management System

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IEEE Internet of Things Journal 2025年第9期12卷 12845-12852页

作者： Tariq, Tayyaba Kuo, Wen-Chung Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J. F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency. © 2014 IEEE.

关键词： Authentication Protocol

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VLC positioning in low data rate for V2V communication 2024

VLC positioning in low data rate for V2V communication

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12th Euro-American Conference on Telematics and Information Systems, EATIS 2024

作者： Giraldo, Luis Miguel Soler, Joaquin Perez Botella-Mascarell, Carmen Juan, Vicent Girbes Varea, Sandra Roger Torres, Julio Martos Olcina, Raimundo Garcia Electronical Engineering Department Universitat de València Burjassot Valencia46100 Spain Computer Science Department Informatics Department Universitat de València Burjassot Valencia46100 Spain

ISBN: (纸本)9798400717338

The creation and implementation of a system that bridges visible light communication (VLC) with selected identifiers, envisioned as informational beacons, are outlined. This system is adept at controlling LED panels and orchestrating communication between LED lights and the receiving unit, laying the groundwork for vehicle-to-vehicle (V2V) communication systems tailored for precise positioning. The design and assessment of test-bed requirements cater to the anticipation of future V2V optical wireless communication scenarios. Through analysis, it is demonstrated that commercial outdoor lighting can be effectively utilized for V2V positioning, capable of operating under daylight with a data transmission rate of 100 Kbps. Crucially, the system's ability to accurately detect positioning beacons facilitates the localization of vehicles within designated areas of interest, paving the way for the advancement of V2V communication technologies. This comprehensive approach not only validates the feasibility of using VLC for vehicle positioning but also sets a precedent for future developments in V2V communication systems. © 2024 Owner/Author.

关键词： Optical communication

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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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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

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

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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A Comparative Study of Physics-Informed and Conventional Neural Networks for Predicting On-Screen Gaze Points from Eye-Tracking Data

SSRN

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SSRN 2025年

作者： Roumpas, Konstantinos Michanetzi, Eleftheria-Lito Minas, Dimosthenis Fotopoulos, Angelos Xenos, Michalis Computer Engineering and Informatics Department University of Patras Greece

Physics-Informed Neural Networks (PINNs) take advantage of physical constraints to improve the predictive performance of neural networks. They have demonstrated remarkable success in physics, chemistry, biology, and medicine, sparking interest in their potential applications in other fields, such as Human-computer Interaction (HCI). Despite their versatility, PINNs are underutilized in certain disciplines due to the challenge of defining Partial Differential Equations (PDEs) that adequately constrain specific problems. This study investigates the application of PINNs to predict fixations captured by an eye-tracking device, employing equations that model head and eye movements. The data for this study were collected during an experiment involving an adaptive user interface for pilots, encompassing a variety of fixation points and rapid glances. The results establish PINNs as a superior method, achieving a mean absolute error (MAE) of 0.61 for horizontal predictions (X) and 0.35 for vertical predictions (Y), significantly outperforming the conventional neural network (NN). Additionally, the PINN demonstrated stronger predictive accuracy for vertical gaze dynamics, as evidenced by an [[EQUATION]] of 0.91 compared to 0.85 for the NN. These findings underscore the robustness of the PINN approach in modeling gaze points with enhanced precision and reliability. While conventional neural networks perform adequately, the added benefit of physical constraints in PINNs encourages further exploration of their application in non-natural science domains. These promising results point toward the potential of PINNs in creating more adaptive and context-aware systems for various HCI applications. © 2025, The Authors. All rights reserved.

关键词： Neurons

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Silent Speech Interfaces: Non-Invasive Neuromuscular Signal Processing for Assistive Communication 4

Silent Speech Interfaces: Non-Invasive Neuromuscular Signal ...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Khan, Sallar Ali, Khawaja Usman Qadri, Ali Raza Ali Rizvi, Maisam Technological University Dublin School of Informatics and Cybersecurity Dublin Ireland Sir Syed University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan

ISBN: (纸本)9798331518882

Our research demonstrates a discrete speech system that empowers those afflicted with speech impairments to convey ideas by internally articulating words without producing audible sound. This method harnesses the minimal movements involved in articulation, making it less arduous than conventional vocalization, especially for individuals facing communication challenges. Silent Speech Interfaces (SSIs) form the foundation of our research, enabling the decoding of neuromuscular signals associated with internal speech articulation to facilitate non-invasive and effective communication for individuals with speech impairments. This research entails the utilization of a multi-electrode configuration to capture subtle, low-voltage electrical signals positioned on the surface area of the subject's neck. These signals emerge as a consequence of neural activation within the speech articulator muscles, signifying the internal articulation of words within the oral cavity, accompanied by minimal physical movement. Subsequent to signal acquisition, a comprehensive analysis of the data is conducted using a biosensing board (Cyton), facilitated by the implementation of the OpenBCI GUI interface and visualization via Python plotting tools. © 2025 IEEE.

关键词： Digital signal processing

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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