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A Reliable Federated Learning Server Rotation Algorithm in IoV

IEEE Internet of Things Journal 2025年第11期12卷 15501-15513页

作者： Cai, Xuelian Yang, Junyi Chang, Tianyu Fu, Yuchuan Yu, F. Richard Cheng, Nan Li, Changle Hui, Yilong The State Key Laboratory of Integrated Services Networks Xidian University Shaanxi 710071 China Research Institute of Smart Transportation Xidian University Shaanxi Xi’an710071 China The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Federated Learning (FL) enables the collaborative training of models by users distributed across various locations, transforming traditional data sharing into model sharing. This paradigm holds the promise of facilitating the development of safe, reliable, and accurate driving models within Internet of Vehicles (IoV), with its performance contingent upon the stability of the training process. However, traditional FL relies on a central server for aggregation, which is susceptible to malicious attacks. Moreover, limited communication resources prevent the inclusion of all users in the training process. To resolve issues related to reliability and resource utilization, this paper proposes a reliable Rotating Server Federated Learning (RSFL) algorithm to enhance the security and efficiency of FL. Specifically, we first consider the vehicular topology and participation in FL during their transition, and introduce a server rotation algorithm that incorporates a weighted sum of multiple factors including model training activity, vehicle credibility, speed stability, and distance to augment system security. Additionally, addressing the limitation of server channel resources that can impede FL efficiency, this paper proposes a method to select high-quality users for channel resource allocation by comprehensively considering participation latency, contribution, energy, and channel state during the FL process. This optimizes resource usage at the FL server side and constructs an efficiency-maximization problem for FL to improve the convergence rate. Simulation results confirm that the proposed RSFL algorithm can significantly enhance the security and system efficiency of FL. © 2014 IEEE.

关键词： Federated learning

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Simulation of Radiation Non-Destructive Testing Operations Based on Virtual Reality and the NDE 4.0 Paradigm

Simulation of Radiation Non-Destructive Testing Operations B...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Vladimir Kuvshinnikov Evgeny Kovshov Research Laboratory of Digital Computer Systems and Automation JSC “Research and Development Institute of Construction Technology - Atomstroy” Moscow Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The implementation of digital industrial technologies marks a pivotal step towards maximizing automation in production processes, aligning with the principles of Industry 4.0 and the NDE 4.0 paradigms. One key area of focus is the modeling of radiation non-destructive evaluation operations within a virtual reality framework. This innovative approach allows for the creation and software application of simulators specifically designed for non-destructive testing of various materials and products. In this context, a complex multi-component digital twin of radiographic image is proposed. The digital twin serves as a comprehensive virtual model that mirrors the characteristics and behaviors of its physical counterpart. The images are generated using discrete modeling techniques that utilize both fundamental physical principles and empirical data provided by manufacturers. This dual approach ensures that the resulting radiographic images closely resemble those produced during actual X-ray inspections. One of the spatial factors that affects image sharpness and quality of inspection is also included in the model. By utilizing virtual reality and digital twin technologies, radiation non-destructive testing training becomes more efficient and better aligned with the automation goals of contemporary industrial practices.

关键词： Training Radiography Solid modeling Virtual reality Production Organizations Inspection Digital twins X-ray imaging Testing

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ML-enabled fault control and efficiency improvement in multi-cloud NFV

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International Journal of System Assurance engineering and Management 2025年 1-9页

作者： Krishna, K. Gopala Chheda, Kajal Khurana, Sahil Aggarwal, Prateek Tiwari, Anamika Sinha, Sanjay Kumar Ramanagara District Karnataka Bangalore562112 India Department of ISME ATLAS SkillTech University Maharashtra Mumbai India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Noida Institute of Engineering & Technology Uttar Pradesh Greater Noida India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

Fault control in Network Function Virtualization (NFV) is an intelligent model to enhance continuous service accessibility and dependability by detecting and solving faults across distributed cloud systems. It protects optimal performance and correlated NFV infrastructures by employing proactive observing, automatic recovery processes, and replacement alternatives. This work aims to provide a novel fault control system for multi-cloud NFV supported by machine learning (ML). We intend to analyze Fault, Configuration, and performance (FCP) problems categorize severity to moderate service impact, and optimize multi-cloud utilization. We are leveraging modern technologies to analyze complex correlations and predict failures, enhancing diagnostic accuracy and system effectiveness. We propose an innovative Honey-optimized Gaussian-kernel Support Vector Machine (HBO-GK-SVM) to accurately detect errors and faults that arise in a multi-cloud NFV environment. This study obtained the Telstra Competition Dataset from Kaggle, including the severity of network faults based on time and location. The findings analysis phase was done with various parameters including recall, precision, and AUC curve. We also conducted a comparison assessment with other existing approaches to examine the effectiveness of the suggested framework. The experimental findings demonstrated the proposed approach yielded better outcomes in terms of recall of 96%, precision of 95%, and AUC score of 99.2% than other conventional approaches for detecting faults to optimize efficiency in multi-cloud NFV. © The Author(s) under exclusive licence to The Society for Reliability engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.

关键词： Network function virtualization

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An improved and decentralized/distributed healthcare framework for disabled people through AI models

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Alexandria engineering Journal 2025年 125卷 441-448页

作者： Rathee, Geetanjali Garg, Sahil Kaddoum, Georges Alzanin, Samah M. Hassan, Mohammad Mehedi Department of Computer Science and Engineering Netaji Subhas University of Technology Dwarka Sector-3 New Delhi110078 India Electrical Engineering Department École de technologie supérieure MontréalQC H3C 1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia King Salman Center for Disability Research Riyadh11614 Saudi Arabia

Access to adequate healthcare is critical for everyone, but people with disabilities often face considerable challenges in receiving reliable and timely medical treatment. The Vision 2030 plan in Saudi Arabia intends to change the healthcare system by incorporating new technologies that increase accessibility, efficiency, and service delivery. However, current healthcare systems continue to suffer from delays, inefficient data processing, and accessibility concerns, especially for the visually impaired. This study proposes a more decentralized healthcare system that uses artificial intelligence (AI) and machine learning (ML) models to improve healthcare services for individuals with disabilities. The system achieves real-time data processing, reduces latency, and enhances decision-making accuracy by combining federated learning and zero-shot architectures. Furthermore, smart technologies such as the Internet of Things (IoT) and natural language processing (NLP) provide seamless data collection and analysis, allowing healthcare practitioners to provide prompt and personalized treatment. The suggested solution solves crucial issues such as inefficiencies in data processing, delays in obtaining medical information, and limits in current healthcare processes. This platform improves impaired people's freedom and mobility by delivering remote healthcare solutions using AI-powered diagnostics and real-time monitoring. This study contributes to a more inclusive and efficient healthcare system in Saudi Arabia by bridging the gap between technology and accessibility, which aligns with the Vision 2030 objective of providing fair healthcare services to everyone. © 2025 The Authors

关键词： Electronic health record

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Goal-Oriented Communication, Estimation, and Control Over Bidirectional Wireless Links

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IEEE Transactions on Communications 2025年第5期73卷 3031-3045页

作者： Cao, Jie Kurniawan, Ernest Boonkajay, Amnart Pappas, Nikolaos Sun, Sumei Popovski, Petar School of Electronic and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Institute of Infocomm Research Agency for Science Technology and Research Fusionopolis 138632 Singapore Linköping University Department of Computer and Information Science Linköping58183 Sweden Aalborg University Department of Electronic Systems Aalborg9220 Denmark

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost. © 1972-2012 IEEE.

关键词： Markov processes

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Laser Scribed CNTs/PEEK/TPU Composites Film for High-Performance Stretchable Electrodes

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IEEE Sensors Journal 2025年第10期25卷 16666-16674页

作者： Jiang, Caiyun Tang, Lei Hou, Jian Ding, Guqiao Sheng, Bin University of Shanghai for Science and Technology College of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instrument and System Shanghai200093 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences State Key Laboratory of Materials for Integrated Circuits Shanghai200050 China

In this article, an elastic composite material comprising carbon nanotubes (CNTs), polyether-ether-ketone (PEEK), and thermoplastic polyurethane (TPU) is presented, in which direct laser-induced graphitization was used to fabricate high-performance stretchable electrodes. The introduction of CNTs can enhance the conductivity and mechanical strength of the composites, thus improving the overall performance of the sensors. Flexible strain sensors with porous structures were prepared by appropriate encapsulation methods, which exhibited superior gauge factors (GFs) of 72, 367, and 972 in the 0%–30%, 30%–44%, and 44%–53% strain regions, respectively. Furthermore, it features a rapid response time (100 ms), excellent stability and durability, and is capable of detecting both large-scale human movement and monitoring of small physical signals. In addition, patterned laser-induced graphene interdigital electrodes (LIG-IDEs) were fabricated on composites using laser light. Highly sensitive humidity sensors were obtained using graphene oxide as the humidity-sensing material for respiratory monitoring and noncontact sensing. The thermoplastic properties of TPU allow the straight fiber to be thermoformed into highly stretchable helical electrode, which was of a helix index of 5 and with a high-quality factor (Q = 130) at 1250% strain. In conclusion, the preparation of LIG on stretchable composite films by direct laser scribing shows a promising approach for large-scale fabrication of wearable devices. © 2001-2012 IEEE.

关键词： Humidity sensors

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A Resourceful Energy Management and Improved Security in Wireless Sensor Networks by Optimized Energy-Based Security Prototype

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SN computer Science 2025年第3期6卷 1-13页

作者： Arya, Nancy Deore, Shailesh Shivaji Sharma, Mahendra Verma, Amit Patibandla, R. S. M. Lakshmi Krishna, K. Murali Pareek, Piyush Kumar Department of Computer Science and Engineering School of Engineering & Sciences GD Goenka University Sohna Gurugram India Department of Computer Engineering SSVPS B S DEORE College of Engineering Maharashtra Dhule India DoIT IMT College of Engineering UP Greater Noida India Department of Computer Science University Centre for Research and Development Chandigarh University Gharuan Punjab Mohali India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Electronics Communication Engineering Rajalakshmi Engineering College TN Chennai India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

One must admit that while using a number of remote sensing points with limited nodes, it is crucial to optimize resource usage as intensely as possible and have a secure network with the least compromises, even though WSNs are applied in increasing numbers of industries. When deploying WSN, power utilization and ability to withstand a number of threats and attacks are important factors to be considered. This work presents a technique for a routing protocol that determines the shortest path between two nodes in a network while at the same time trying to conserve energy. Lifetime and energy consumption of the networks are two important factors when designing new protocols for WSN. An important problem of finding a load-sharing routing method with less energy consumption is difficult. TBEBR routing protocol optimizes the parameters that lead to minimal energy dissipation to enhance the lifetime of the network by identifying the path with the least energy consumption possible. Throughput, overhead, packet loss and end to end delay were used as performance parameters to assess the routing protocol. The analysis of the overall efficiency of the routing concept as well as the method of its application was described. As far as the performance of network and nodes is concerned, it is clear that TBEBR outperforms both DSR and AODV. For WSNs, the proposed routing protocol aims at minimizing the energy issue, which is a disadvantage of the technology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DoS attack EWMA Unit energy requirement WSN

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A Generalized ChatGPT-Based Collaborative Multi-Objective Decision-Making Framework for Robust Vehicle Platoon Collision Avoidance

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7212-7225页

作者： Zhang, Hanwen Hou, Chenfei Chen, Jicheng Zhang, Hui Wang, Fei-Yue Beihang University Hangzhou Innovation Institute Hangzhou310056 China Beihang University School of Transportation Science and Engineering Beijing100191 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China Obuda University Physiological Controls Research Center University Research and Innovation Center Budapest1034 Hungary Macau University of Science and Technology Macau Institute of Systems Engineering Macau999078 China

In this work, we propose a ChatGPT-based framework that collaborates with human expert instructions for safe and efficient vehicle platoon control. The collaborative framework integrates multi-objective decision-making to address challenges such as robust collision avoidance in complex traffic environments. To incorporate human- like reasoning, ChatGPT is firstly used to interpret human expert driving instructions, which often involve linguistic variables. It generates behavioral decisions and computes feasible trajectories for each vehicle to ensure driving safety. A distributed model predictive controller is then utilized for accurate, real-time path tracking. To efficiently handle ChatGPT response failures, a receding instruction utilization strategy is proposed. For critical response failures, hindsight experience replay is used to supplement ChatGPT's knowledge database and improve response accuracy in complex scenarios. Simulation tests and experimental validations demonstrate the framework's effectiveness and generalizability across various traffic conditions, highlighting its potential for broader applications in platoon control. © 1967-2012 IEEE.

关键词： Reinforcement learning

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research on Tumor Images Segmentation based on Improved Convolutional Neural Network 24

Research on Tumor Images Segmentation based on Improved Conv...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Software defined network (SDN) based data server computing system

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International Journal of Information Technology (Singapore) 2025年第1期17卷 607-613页

作者： Madhura, K. Sekhar, Gillala Chandra Sahu, Amaresh Karthikeyan, M.P. Khurana, Saniya Shukla, Meenu Vashisht, Nitish Information and Communication Technology Manipal Institute of Technology Mahe Bengaluru India Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India Ajay Binay Institute of Technology Odisha India Department of Computer Science and Information Technology Jain (Deemed to be University) Karnataka Jain Bangalore India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Rajpura India

The growing clientele and broadband demand lead to several technical restrictions in ensuring stable connections between data centers. The data on existing network devices such as switches, routers, and gateways must be routed to some particular point commonly known as software-defined network (SDN). SDN is defined as a networking approach that makes use of software-based controllers or application programming interfaces (API’s) to communicate with existing hardware and provide paths to traffic in the network. In order to overcome technical restrictions in the network, the given paper introduces an application-based enabled open flow information center network layout to accomplish transport load allocation and higher efficiency. In order to mitigate noise in the network, the proposed approach employs a data centre network (DCN) and implements it using SDN controller such as OpenDaylight (ODL). ODL is an open-source platform for automating networks of any size and scale. It allocates traffic requests among the linked servers, thereby ensuing higher throughput, optimization of load balancing, and versatility in SDN’s. Lastly, the performance of the proposed approach is validated by assessing the throughput rate (Mbps) and average delay (ms) prior to load balancing (BLD) and after load balancing (ALD). The results show that the proposed approach leads to higher throughput after load balancing optimization using ODL. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： DCN Load balancing ODL SDN Throughput

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