Internet Technology Letters最新文献_第2页

Zero Trust Networks and Federated Unlearning Based 6G Edge Networks: Attack Scenario, Security Model and Future Directions 零信任网络和基于联邦学习的6G边缘网络：攻击场景、安全模型和未来方向

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Internet Technology Letters Pub Date : 2025-06-22 DOI: 10.1002/itl2.70056

Nishat Mahdiya Khan, Pronaya Bhattacharya, Haipeng Liu, Zhu Zhu, Thippa Reddy Gadekallu

引用次数: 0

Online Auxiliary Evaluation of Physical Education Teaching Based on Facial Expression Recognition 基于面部表情识别的体育教学在线辅助评价

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Internet Technology Letters Pub Date : 2025-06-19 DOI: 10.1002/itl2.70065

Yuan Gao

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Modeling of 5G Based OAM-RoF System Using ZCC OCDMA Code With TDM Over Fiber-FSO Link 基于光纤- fso链路的ZCC OCDMA码与TDM的5G OAM-RoF系统建模

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Internet Technology Letters Pub Date : 2025-06-18 DOI: 10.1002/itl2.70023

Meet Kumari

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A Machine Learning Framework for Enhancing 5G mmWave Radio Frequency Prediction 增强5G毫米波射频预测的机器学习框架

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Internet Technology Letters Pub Date : 2025-06-16 DOI: 10.1002/itl2.70057

Shantha Mary Joshitta, Dukhbhanjan Singh, Sagar Gulati, Pooja Sapra, Romil Jain, Diksha Aggarwal

{"title":"A Machine Learning Framework for Enhancing 5G mmWave Radio Frequency Prediction","authors":"Shantha Mary Joshitta, Dukhbhanjan Singh, Sagar Gulati, Pooja Sapra, Romil Jain, Diksha Aggarwal","doi":"10.1002/itl2.70057","DOIUrl":"https://doi.org/10.1002/itl2.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>5G mmWave technology offers high data rates and bandwidth, but high path loss and environmental changes affect signal quality. Existing models are not suitable for mmWave channels due to their varying nature over time. To overcome these challenges, this research presents an efficient time-dependent channel modeling framework based on a cuttlefish search-inspired efficient support vector machine (CS-ESVM) for predicting channel characteristics in large-scale measurement and RF at specific measurements at LOS and NLOS. The model is proposed to work for measurements at 28 GHz at a substation. The model also combines a prediction model and playback model for accurate channel characteristics key metrics such as root mean square error (RMSE), mean absolute percent error (MAPE), and correlation coefficient (CC), predicting the radio frequency. The proposed CS-ESVM model achieved the lowest RMSE values of 2.510 (LOS corridor), 1.210 (LOS hall), 1.815 (NLOS corridor), and 1.917 (NLOS hall), the lowest MAPE values of 0.009, 0.004, 0.003, and 0.007, and the highest CC values of 0.899, 0.969, 0.921, and 0.985. The findings suggest that CS-ESVM is more effective at predicting the mmWave channel's characteristics than traditional approaches. In conclusion, this ML-based framework improves the projection of 5G mmWave RF channels and provides a stable solution for real-time prediction in future network environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge Computing Oriented Decision and Optimization Method for Efficient and Intelligent Human Resource Management and Analysis 面向边缘计算的高效智能人力资源管理与分析决策与优化方法

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Internet Technology Letters Pub Date : 2025-06-12 DOI: 10.1002/itl2.70054

Meiyi Lin

{"title":"Edge Computing Oriented Decision and Optimization Method for Efficient and Intelligent Human Resource Management and Analysis","authors":"Meiyi Lin","doi":"10.1002/itl2.70054","DOIUrl":"https://doi.org/10.1002/itl2.70054","url":null,"abstract":"<div>\u0000 \u0000 <p>Modern enterprises face significant challenges in achieving real-time, intelligent workforce management due to the limitations of centralized cloud-based solutions in dynamic operational environments. This paper proposes an edge computing-oriented decision and optimization method for efficient and intelligent human resource management and analysis. First, we design a hierarchical edge-cloud architecture comprising infrastructure, edge, cloud, and application layers, specifically optimized for workforce data processing through localized decision modules. Second, we develop a TinyML-enhanced multi-objective optimization method that concurrently addresses the intelligent HR data sentiment analysis and optimal resource decision towards privacy and latency minimization, as well as F1 score maximization. Specifically, we establish the data analysis model, based on which we construct the problem as a multi-objective decision model to be addressed and obtain the optimization solution. Lastly, we carry out rich experiments which show that the proposed method achieves better performance than the compared methods, including achieving the F1 score over 90% and reducing the population size of model parameters greatly.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Machine Learning Architecture for Traffic Grooming and Resource Optimization in 5G Optical Fronthaul 5G光前传流量疏导与资源优化的新型机器学习架构

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Internet Technology Letters Pub Date : 2025-06-09 DOI: 10.1002/itl2.70052

Aiman Mailybayeva, Saurabh Jain, Jaspreet Sidhu, Nitish Vashisht, Narmadha Thangarasu, Saumya Goyal

{"title":"A Novel Machine Learning Architecture for Traffic Grooming and Resource Optimization in 5G Optical Fronthaul","authors":"Aiman Mailybayeva, Saurabh Jain, Jaspreet Sidhu, Nitish Vashisht, Narmadha Thangarasu, Saumya Goyal","doi":"10.1002/itl2.70052","DOIUrl":"https://doi.org/10.1002/itl2.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>The emergence of 5G networks has emerged as innovative solutions for traffic grooming and resource management in optical fronthaul networks. Traditional methods are often incapable of managing the complexity of different traffic patterns, low latencies, and high bandwidth consumption, which leads to suboptimal resource allocation and, consequently, high operating costs. The objective is to develop an innovative machine learning (ML) architecture called Intelligent Multi-Attentive Generative Adversarial Networks (IMAGAN) for maximizing resource utilization and traffic grooming (TG) in 5G optical fronthaul networks. The suggested IMAGAN-based architecture consists of a multi-attentive model for identifying spatiotemporal traffic patterns combined with a generative adversarial model to provide synthetic network scenarios. The findings indicate that the IMAGAN-based architecture enhances the performance of energy management systems in terms of resource utilization ratio, bandwidth utilization ratio, rejection ratio, MAE, and RMSE. The findings of the study offer a strong foundation for further improvements in intelligent 5G network design and management.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TinyML Induced Portable Food Defect Detection for Edge Computing TinyML诱导的便携式食品缺陷检测边缘计算

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Internet Technology Letters Pub Date : 2025-06-04 DOI: 10.1002/itl2.70044

Yanxia Liu

引用次数: 0

Lightweight Federated Learning for Energy-Efficient English Corpus Distribution and Optimization in Edge-Cloud Collaboration Networks 轻量级联合学习在边缘云协作网络中节能英语语料库分布和优化

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Internet Technology Letters Pub Date : 2025-06-02 DOI: 10.1002/itl2.70051

Bin Yang

{"title":"Lightweight Federated Learning for Energy-Efficient English Corpus Distribution and Optimization in Edge-Cloud Collaboration Networks","authors":"Bin Yang","doi":"10.1002/itl2.70051","DOIUrl":"https://doi.org/10.1002/itl2.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>This study introduces an energy-aware collaborative architecture that synergistically converges edge computing resources, cloud infrastructure, and privacy-preserving distributed learning mechanisms for optimized English corpus distribution. The proposed framework systematically implements lightweight federated learning through a three-tier optimization paradigm. In particular, by combining federated learning with edge-cloud architecture, we can aggregate the edge information easily and execute the federated learning model naturally, so as to improve performance. In addition, various strategies have been introduced to lightweight the model from the aspects of devices, structure, and quantization. Hence, the lightweight feature is naturally supported in this proposed framework. The proposed framework and method are implemented and tested via comprehensive experiments. The corresponding results indicate we have achieved great performance, including an 83% reduction in energy consumption and a 76% reduction in latency, which state that the proposed method outperforms the state-of-the-art methods.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Reliability of Smart Healthcare Monitoring Systems via Network Coding in Wireless Body Area Networks 基于无线体域网络编码的智能医疗监控系统可靠性优化

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Internet Technology Letters Pub Date : 2025-06-01 DOI: 10.1002/itl2.70009

Maha Dev, Saurabh Singh, Abhishek Jain, Ashok Kumar, Jitendra Kumar Chaudhary

引用次数: 0

Efficient UAV Image Recognition Mechanism for Digital Management System in Wireless Communication Networks 无线通信网络中数字化管理系统的高效无人机图像识别机制

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Internet Technology Letters Pub Date : 2025-06-01 DOI: 10.1002/itl2.70050

Zhenxian Yao, Ge Song, Jinyang Zhao

引用次数: 0