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28th conference on Innovation in Clouds, internet and networks, ICIN 2025

作者： Hu, Weiwei Ohsita, Yuichi Shimonishi, Hideyuki Graduate School of Information Science and Technology Osaka University Japan D3 Center Osaka University Japan

ISBN: (纸本)9798331542399

With the integration of 6G networks and internet of Vehicles (IoV), constructing reliable Transmission control Protocol (TCP) throughput maps is crucial for intelligent transportation systems. These maps provide critical insights into network performance, enabling efficient data transmission planning and informed decision-making. However, the dynamic nature of IoV systems, along with resource constraints poses challenges for map construction. To address these challenges, we develop a novel framework for constructing TCP throughput maps. The framework uses base station sensing to collect vehicle positions and communication resources to gather TCP throughput data. A Digital Twin integrates those data over the map in real-time and predicts TCP throughput at every location with prediction uncertainty. A key aspect of this framework is the selection of vehicles to measure TCP throughput, minimizing prediction uncertainty and measurement overhead. We present a method that models the TCP throughput map using Gaussian Process Regression, which accounts for uncertainty. And to take into account the dynamic nature of IoV environments, we also propose the Fixed-Observation Rolling Optimization Algorithm (FOROA) to dynamically select vehicles that measure communication performance. Simulation experiments show that the FOROA reduces map uncertainty 41% faster than the previous method when reducing map uncertainty by 40%. © 2025 IEEE.

关键词： Transmission control protocol

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A Lightweight Steel Strip Defect Detection Method Based on YOLO 43

A Lightweight Steel Strip Defect Detection Method Based on Y...

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43rd Chinese control conference, CCC 2024

作者： Qian, Hao Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

As an essential component of modern industry, steel strips play an indispensable role in various fields and serve as a crucial raw material in industrial production. However, due to various factors such as production technology, the surface of steel strips often exhibits unavoidable defects like scratches and crazings. Therefore, implementing online surface quality inspection for steel strips is one effective way to enhance product quality and production efficiency. This paper proposes a detection method for surface defects in steel strips based on YOLO. By optimizing the network structure of YOLOv5s, the performance on the NEU-DET dataset has improved by nearly 4 % compared to the original network's mAP value. Experimental results indicate that the detection performance of this method is significantly superior to the current mainstream lightweight detection networks. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Strip metal

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Ensemble Learning for network Data Classification with SDN Clustering Underlying Smart Power Grid-Enabled Smart Cities 3

Ensemble Learning for Network Data Classification with SDN C...

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3rd IEEE International conference on Power Electronics, Intelligent control and Energy systems, ICPEICES 2024

作者： Patel, Man Jain, Naman Patel, Janam Ramoliya, Fenil Gupta, Rajesh Tanwar, Sudeep Institute of Technology Nirma University Department of Comp. Sci. & Engg. Ahmedabad India

ISBN: (纸本)9798350387193

Smart power grid enables smart city to be operated on efficient level for sustainable urban planning, economic growth and become an innovation hub. Various types of energy user or provider plants can contribute to providing services while integrated with physical sensor devices, modules, and software interfaces for efficient communication and control. These integrated software interfaces and computer devices also help us during response for any catastrophic condition, fault detection-diagnosis, remote operation-maintenance, integration with Supervisory control and Data Acquisition (SCADA) systems and provide robust cyber security measures. Software defined network (SDN) allows us to have traffic prioritization, traffic engineering and load balancing, quality of Service (QoS) enforcement and logical segmentation. However, relying on a single SDN entity to handle all network traffic can introduce several challenges such as single point of failure, scalability limitation, limited redundancy, increased complexity and management overhead and delay in serving of highly critical requests during high-load condition. To mitigate this paramount concern, we propose Ensemble Learning (EL)-based network traffic classification approach with clustering of SDNs to carry-out tasks in efficient and effective manner. The performance evaluation of proposed approach has been done through various performance metrics such as receiver operating characteristic (ROC) curve, precision, recall, f'1-score, PR curve and confusion matrix. © 2024 IEEE.

关键词： quality of service

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CCIOT 2024 - Proceedings of 2024 9th International conference on Cloud Computing and internet of Things

CCIOT 2024 - Proceedings of 2024 9th International Conferenc...

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9th International conference on Cloud Computing and internet of Things, CCIOT 2024

ISBN: (纸本)9798400717161

The proceedings contain 18 papers. The topics discussed include: research on RedCap UE’s performance indicators in real network to support IoT applications;machine learning-based crop recommendation for IoT-enabled smart agriculture;cloud provisioning: a highly optimized, cost-effective, and efficient deployment model;smart pill box: an IoT-integrated application for monitoring patient medication usage at home;analyzing cybersecurity risk with a phishing simulation website;agile infrastructure: adapting systems to changing business requirements;a secure and efficient privacy scheme for location-based services in cloud environments;and research on garbled circuits-based identity authentication and ciphertext hidden access control in vehicular networks.

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Improved Video QoE in Wireless networks using Deep Reinforcement Learning 19

Improved Video QoE in Wireless Networks using Deep Reinforce...

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19th International conference on network and Service Management (CNSM) - network and Service Management in the Era of Generative AI and Digital Twins

作者： Moura, Henrique D. Oliveira, Junia Maisa Soares, Daniel Macedo, Daniel F. Vieira, Marcos A. M. Univ Antwerp IDLab Imec Antwerp Belgium Univ Fed Minas Gerais Comp Sci Dept Belo Horizonte MG Brazil

ISBN: (纸本)9783903176591

Millions of videos are watched per minute on the internet. Due to real-time performance demands, such as high-quality video streaming, network administrators face new challenges to control the network and cope with the expected quality of experience (QoE). Automatic control is a necessity to reduce the OPEX, because it could reduce the need for resource overprovisioning, as well as the number of human administrators. Dynamic rate in video streaming alleviates the resource usage, but it worsens the video quality when a network bottleneck occurs, lowering the QoE. This paper dynamically adjusts the IEEE 802.11 parameters to improve the network condition and hence maintain a higher QoE. While traditional networks are not aware of the application, in our proposal the controller learns the configuration of the access points (APs) (in terms of transmission power and channel number) that provide the best QoE, using double deep Q-learning (DDQL). The proposal improves video QoE by 91% in the best case, when compared to three baselines. It also balances the QoE among clients, improving the fairness up to 115% when compared to the baselines.

关键词： Deep Reinforcement Learning Adaptive control Wireless networks quality of Experience

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Analysis of Power Communication network And Power Information Security Based on Data Mining Algorithm Under Edge Collaboration Architecture

Analysis of Power Communication Network And Power Informatio...

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2023 International conference on Power, Electrical Engineering, Electronics and control, PEEEC 2023

作者： Raoa, Qiang Huangb, Jie Zhangc, Ke Jind, Jing State Grid Hubei Electric Power Company Hubei Wuhan430077 China

ISBN: (纸本)9798350329124

With the proposal of 'internet plus' and the penetration of internet technology in all fields of society, the importance of communication information network security has become increasingly prominent. Communication information data dissemination has brought unprecedented new challenges and problems to national security and social stability. According to the isolation and authentication requirements of information security protection of power grid enterprises, based on the research of the relevant smart grid business scenarios, system architecture and security requirements with wireless public network as the channel, the monitoring architecture of wireless public network channel for intelligent power distribution is designed, including the monitoring scheme of intelligent power distribution business terminal, system master station, wireless public network channel and other links. In order to ensure the safe and reliable operation of power communication access network, encryption, authentication, isolation and other security protection measures are mainly adopted. Another important index to measure the network performance of power communication network is the quality of service ($Q_performance$S), which will be sacrificed at the cost of network security. This paper proposes a method of information system security situation assessment based on DM. Situation assessment is one of the most important measures to detect the security of information systems, which can provide power system security status information for managers. Managers can obtain the abnormal problems in the system and take targeted measures to improve the security performance of the system. © 2023 IEEE.

关键词： network security

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Wireless IoT Monitoring System in Hong Kong-Zhuhai-Macao Bridge and Edge Computing for Anomaly Detection

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IEEE internet OF THINGS JOURNAL 2024年第3期11卷 4763-4774页

作者： Wang, Xiaoyou Wu, Wanglin Du, Yao Cao, Jiannong Chen, Qianyi Xia, Yong Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Artificial Intelligence Things Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

The emergence of the internet of Things (IoT) has facilitated the development and usage of low-computational microcontrollers at the edge of the network, which process data in the proximity of data sources and thereby offload the pressure of data transmission. Recently, IoT is becoming a key technology for structural health monitoring (SHM) systems. This study designs a novel wireless IoT monitoring system for the Hong Kong-Zhuhai-Macao Bridge, the world longest sea-crossing bridge. The 5G technology and edge computing are integrated to improve the system performance in sensor serviceability, data transmission, time synchronization, and data quality control. The artificial intelligent (AI) algorithm is embedded into the NVIDIA Xavier NX edge computing boards to preliminarily detect data anomalies caused by sensor faults, before uploading the massive data to the cloud platform. As training AI models requires a large amount of labeled data and is always time consuming, a novel data anomaly detection method is developed by transferring the model trained from the other bridge to the target bridge. Given that prestoring source data in edge devices consumes expensive storage resources, the source-free domain adaptation is developed by integrating the robust self-training mechanism and self-knowledge distillation strategy. Thus, the model transfer is achieved cross bridges in the absence of source data. This study provides a valuable and practical reference for developing a wireless IoT SHM system for large-scale infrastructure and enabling edge computing for data anomaly detection with high efficiency and accuracy.

关键词： Anomaly detection edge computing internet of Things (IoT) source-free domain adaptation structural health monitoring (SHM)

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Enhancing IoT network Intrusion Detection with a New GraphSAGE Embedding Algorithm Using Centrality Measures 10

Enhancing IoT Network Intrusion Detection with a New GraphSA...

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10th International conference on internet of Things, Big Data and Security, IoTBDS 2025

作者： Termos, Mortada Ghalmane, Zakariya Brahmia, Mohamed-El-Amine Fadlallah, Ahmad Jaber, Ali Zghal, Mourad CESI LINEACT UR 7527 Strasbourg France Computer Science Department Faculty of Sciences Lebanese University Beirut Lebanon Beirut Lebanon

ISBN: (纸本)9789897587504

The rapid expansion of the internet of Things (IoT) has led to many opportunities in addition to introducing complex security challenges, necessitating more powerful network Intrusion Detection systems (NIDS). This study addresses this challenge by enhancing Graph Neural networks (GNNs) with centrality measures to improve intrusion detection performance in IoT environments. We propose the so-called"Centrality-based E-GraphSAGE", an extension to the E-GraphSAGE model incorporating the centrality measures: Degree, Betweenness, Closeness, PageRank, and K-truss. These centrality measures, which highlight both the local and global influence of nodes (IoT devices), can uncover hidden patterns and relationships in network traffic data, thereby enhancing the performance of IDS systems. The centrality-informed initialization of node embeddings aids the model in capturing critical structural insights in the graph. The inclusion of residual connections further improves classification accuracy. Our models were evaluated on four datasets: NF-UQ-NIDS, NF-CSE-CIC-IDS2018, CCD-INID, and X-IIoTID. Results showed significant performance gains in accuracy of detection evaluated using F1-score and reduced number of false alarms. This work paves the way for more advanced and robust intrusion detection systems to improve the security of IoT networks. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Graph neural networks

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Research on Auxiliary Terminal System of Power Communication Operation and Maintenance Based on Augmented Reality Technology 6

Research on Auxiliary Terminal System of Power Communication...

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6th IEEE International conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Hao, Xue Geng, Lizhuo Liu, Lu Wang, Xunshi Lin, Chunlong Fan, Yunkun State Grid Hebei Electric Power Company Limited Training Center Shijiazhuang China Beijing Kedong Electric Power Control System Company Limited Simulation Branch Beijing China

ISBN: (纸本)9798350374315

An intelligent operation and maintenance system of power communication network based on the internet of Things is established. The mobile operation management system of the internet of Things is used to accurately collect, process and configure the mobile communication data of electric energy. The power intelligent communication operation management system and the internet of Things data configuration processing platform are organically combined to achieve the improvement and optimization of the power communication mobile intelligent system. A new algorithm using Bayesian algorithm to detect suspicious faults in power networks;Then, using the data obtained from the integrated RRPMS, the alarm of the communication network management system, and the scheduling integrated operation management OBS, the effective control of the accident is realized. The test shows that the mobile phone network operation and maintenance system designed in this project can meet the actual demand, and its working reliability is greater than 80%. Compared with the conventional method, its performance is improved by 20% to 30% to achieve the purpose of standardization and intelligence. The system improves grid operational efficiency. © 2024 IEEE.

关键词： Information management

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Neuronal networks for Visual Inspection of Assembly Completeness and Correctness in Manufacturing 7th

Neuronal Networks for Visual Inspection of Assembly Complete...

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7th International conference on Green Technology and Sustainable Development

作者： Baykar, Ali Omer Kural, Ayhan Lambrecht, Jens Turkish German Univ Istanbul Turkiye Tech Univ Berlin Berlin Germany Istanbul Tech Univ Istanbul Turkiye

ISBN: (纸本)9783031761966;9783031761973

The conformity of the quality with the desired specifications in production must be controlled quickly, reliably and accurately. Cost reduction and efficiency studies in production quality control stages are of great importance today. For this reason, non-human and intelligent automated systems are the main research subjects as a solution method in quality control stages. In this study, the final visual inspection of fastening elements of an industrial product is addressed. The inspection of connection elements, such as screws, as one of these quality control stages, is presented through a framework utilizing a camera and learnable neural network, replacing human-eye control. Fasteners can be counted as small objects in the images obtained. Therefore, in this study, object detectors based on different CNN backbones (ResNet 50-101) and proposals are discussed and their performance in detecting these small objects is compared to achieve the high detection speed, accuracy and reliability. To address the challenges at an industrial level for object detection methods, a non-processed image dataset has been created. This dataset aims to represent various lighting conditions, including dark-bright fields and diffuse reflection, as well as occlusion and restricted camera angles. During the training phase, hyperparameter-tuning optimization of deep networks such as YOLOv8, Faster-RCNN with ResNet50&101 and lastly Sparse-RCNN with a different set of learned object proposals is evaluated, which can be most suitable for the detection of screw connection. Experimental results show that the pretrained Faster-RCNN and Sparse RCNN has over the % 85 success rate of detection of small objects in an industrial environment.

关键词： Visual Inspection Convolutional Neural network Object Detection Screw Connection Small Object Detection

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