Internet Technology Letters最新文献

{"title":"Modeling of 5G Based OAM-RoF System Using ZCC OCDMA Code With TDM Over Fiber-FSO Link","authors":"Meet Kumari","doi":"10.1002/itl2.70023","DOIUrl":"https://doi.org/10.1002/itl2.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>A 32 GHz radio-over-fiber (RoF) system using integrated mode division multiplexing (MDM) and optical code division multiple access (OCDMA) scheme is realized. Quad orbital angular momentum (OAM) based zero cross-correlation OCDMA coded signals with time division multiplexing are transmitted over hybrid fiber-free space optics (FSO) link under the impact of clear air, haze, light, and moderate fog conditions. Simulation results depict that the system offers a maximum 0.1 km fiber and 500 m FSO range at aggregate 30 Gbps throughput. Also, the system offers acceptable bit error rate performance with a maximum 14 μm spot size, 14 cm transmitter/receiver aperture diameters, and a minimum received power of −2 dBm. Compared to existing designs, this work offers optimum system performance.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309036","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}

{"title":"A Machine Learning Framework for Enhancing 5G mmWave Radio Frequency Prediction","authors":"Shantha Mary Joshitta,&nbsp;Dukhbhanjan Singh,&nbsp;Sagar Gulati,&nbsp;Pooja Sapra,&nbsp;Romil Jain,&nbsp;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}

{"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}

{"title":"A Novel Machine Learning Architecture for Traffic Grooming and Resource Optimization in 5G Optical Fronthaul","authors":"Aiman Mailybayeva,&nbsp;Saurabh Jain,&nbsp;Jaspreet Sidhu,&nbsp;Nitish Vashisht,&nbsp;Narmadha Thangarasu,&nbsp;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}

{"title":"TinyML Induced Portable Food Defect Detection for Edge Computing","authors":"Yanxia Liu","doi":"10.1002/itl2.70044","DOIUrl":"https://doi.org/10.1002/itl2.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Food defect detection is a critical link in ensuring food safety. Traditional machine vision-based detection systems rely on cloud servers to complete algorithmic inference, suffering from problems such as large detection equipment volume and insufficient real-time performance. This paper proposes a portable lightweight detection scheme based on Terminal Machine Learning (TinyML) and Mobile Edge Computing (MEC). Through lightweight neural network model compression technology and edge node task collaboration mechanisms, low-power operation and millisecond-level response of the detection equipment are achieved. Experimental results show that in the scenario of fruit surface defect detection, the system achieves a detection accuracy of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>95.2</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$$ 95.2% $$</annotation>\u0000 </semantics></math>, with a single-frame inference power consumption of only 85 mW, meeting the practical application requirements of portable devices.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206656","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}

{"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}

{"title":"Optimizing Reliability of Smart Healthcare Monitoring Systems via Network Coding in Wireless Body Area Networks","authors":"Maha Dev,&nbsp;Saurabh Singh,&nbsp;Abhishek Jain,&nbsp;Ashok Kumar,&nbsp;Jitendra Kumar Chaudhary","doi":"10.1002/itl2.70009","DOIUrl":"https://doi.org/10.1002/itl2.70009","url":null,"abstract":"<div>\u0000 \u0000 <p>A Wireless Body Area Network (WBAN) is a system that connects a variety of self-contained medical sensors and devices positioned both internally and externally on the human body. The reliability of the network is an important consideration in the design of a WBAN, especially for healthcare applications. Because of the network's close proximity to the human body, electromagnetic waves should have a low effect on the body. In a WBAN, sensors do not send their data directly to monitoring devices or gateways; instead, the data is transmitted via relay nodes. A new smart healthcare monitoring reliability system based on cooperative network coding (RCNC) is presented. Simulation results demonstrate that RCNC-WBAN schemes can achieve optimal results while improving packet delivery, reducing resource redundancy degree (RRD), and minimizing delay.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191088","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}

{"title":"Efficient UAV Image Recognition Mechanism for Digital Management System in Wireless Communication Networks","authors":"Zhenxian Yao,&nbsp;Ge Song,&nbsp;Jinyang Zhao","doi":"10.1002/itl2.70050","DOIUrl":"https://doi.org/10.1002/itl2.70050","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless networks have emerged as a transformative solution to address the challenges of low matching accuracy and insufficient precision in unmanned aerial vehicle (UAV) patrols and image recognition within digital management systems. This work proposes a UAV-oriented patrol inspection system for digital management, leveraging the ultra-low latency and high bandwidth of wireless communication networks to significantly enhance real-time data processing and transmission efficiency. A rapid feature matching algorithm is introduced for patrol image identification, enabling scene image processing and feature point extraction from UAV-captured imagery. Experimental results demonstrate the algorithm's superior recognition accuracy in digital systems, achieving 96% precision under high feature point density conditions.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190987","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}

{"title":"An A3C Learning Approach for Adaptive Service Function Chain Placement in Softwarized 5G Networks","authors":"Anjali Rajak,&nbsp;Rakesh Tripathi","doi":"10.1002/itl2.70021","DOIUrl":"https://doi.org/10.1002/itl2.70021","url":null,"abstract":"<div>\u0000 \u0000 <p>Network functions virtualization transforms traditional network functions into software, enabling them to run as virtual network function (VNF) instances on cloud infrastructure. In softwarized 5G networks, communication services are provided through service function chains (SFCs), which sequentially link multiple VNFs according to specific requirements. This approach enhances network management and orchestration, offering greater flexibility and scalability. However, improving resource consumption and quality of service while adhering to physical network constraints remains a significant challenge. This study introduces an A3C-GLA framework for adaptive service function chain placement (that leverages the Asynchronous Advantage Actor Critic (A3C) algorithm, Graph Attention Networks (GATs), and Sequence-to-Sequence Long Short-Term Memory with Attention mechanism (Seq2SeqLSTM-A). Extensive simulations demonstrate that the proposed framework significantly outperforms existing benchmark schemes in terms of long-term average revenue and acceptance ratio, offering a more efficient and effective solution for SFC placement in softwarized 5G networks.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191089","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}

{"title":"Self-Supervised Learning Framework With Under-Balanced Loss Optimization for Point of Care MRI Image Reconstruction in 6G-Driven Edge Networks","authors":"Yang Liu","doi":"10.1002/itl2.70035","DOIUrl":"https://doi.org/10.1002/itl2.70035","url":null,"abstract":"<div>\u0000 \u0000 <p>Self-supervised learning frameworks in the 6G-driven edge networks provide powerful instant MRI image diagnostic capabilities for the process of point of care. Although many deep learning self-supervised frameworks are used to train-related models to improve magnetic resonance imaging (MRI) image reconstruction, there is still room for improvement in model training convergence acceleration and MRI image reconstruction quality. To address the above issues, first, this article proposes a self-supervised learning framework, which combines the real-time computing power of the edge network driven by 6G networks to accelerate the training convergence of the MRI image reconstruction model and improve the quality of the reconstructed image. Second, the proposed framework innovatively introduces an under-balanced loss optimization structure and applies heterogeneous loss functions at different positions of the model. Finally, this article proposes AttentionFISTA-Net, which integrates the convolutional attention module into FISTA-Net to enhance the MRI image reconstruction effect. Experimental results on the IXI dataset compared with the traditional self-supervised network show that the proposed model performs better in the under-sampled dataset with acceleration rates of 4 and 8, respectively. The peak signal-to-noise ratio (PSNR) metric improves <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.021</mn>\u0000 </mrow>\u0000 <annotation>$$ 0.021 $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.61</mn>\u0000 </mrow>\u0000 <annotation>$$ 0.61 $$</annotation>\u0000 </semantics></math> respectively, and the structure similarity index measure (SSIM) metric improves <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.5</mn>\u0000 <mo>*</mo>\u0000 <msup>\u0000 <mn>10</mn>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {0.5}^{ast }{10}^{-3} $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>8.2</mn>\u0000 <mo>*</mo>\u0000 <msup>\u0000 <mn>10</mn>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {8.2}^{ast }{10}^{-3} $$</annotation>\u0000 </semantics></math>, respectively.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190990","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}