Internet Technology Letters最新文献_第9页

Impulse Generation and Motion Tracking of Rocket Bodies Using Wearable Sensors in 5G/6G Networks 基于5G/6G网络可穿戴传感器的火箭机体脉冲产生与运动跟踪

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Internet Technology Letters Pub Date : 2025-07-07 DOI: 10.1002/itl2.70068

B. Ravi Chandra, Ajay Roy, Mohammed I. Habelalmateen, Shahad Almansour, Sudan Jha

{"title":"Impulse Generation and Motion Tracking of Rocket Bodies Using Wearable Sensors in 5G/6G Networks","authors":"B. Ravi Chandra, Ajay Roy, Mohammed I. Habelalmateen, Shahad Almansour, Sudan Jha","doi":"10.1002/itl2.70068","DOIUrl":"https://doi.org/10.1002/itl2.70068","url":null,"abstract":"<div>\u0000 \u0000 <p>Wearable sensor technologies and wireless networks, particularly in 5G and 6G networks, have transformed data transport and real-time monitoring in many industries. In this work, we present a unique method to improve impulse generation analysis and motion detection using wearable sensors inside a wireless network. Low-latency 5G/6G communication architectures and sophisticated sensor nodes are used in the proposed system to continually monitor dynamic parameters and send important motion data with the lowest delay. Inspired by propulsion-based motion studies, we investigate impulse generation with a modified sugar-based composite propellant comprising potassium nitrate (KNO<sub>3</sub>), powdered sugar (C<sub>12</sub>H<sub>21</sub>), and potassium sulfide (K<sub>2</sub>S). These propellants experience combustion, making reaction by-products (N<sub>2</sub> + 3CO<sub>2</sub>). This compound sets the foundation for motion detection analysis, which is used in many controlled propulsion experiments. By enabling real-time impulse measuring and motion tracking, wearable wireless sensors help with data gathering, predictive modeling, and decision-making. We evaluate the dependability and efficiency of the system by comparing it with current motion detection and wireless communication architectures. Experimental results show that the suggested data processing and prediction approach significantly improves impulse detection and motion tracking. The results help to forward wearable sensor-based wireless networks for aerospace, industrial automation, and biomedical applications in next-generation 5G/6G networks.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573683","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 Based Edge Intelligent English Classroom Quality Assessment Scheme 基于TinyML的边缘智能英语课堂质量评价方案

IF 0.9

Internet Technology Letters Pub Date : 2025-07-07 DOI: 10.1002/itl2.70072

Shuang Jiang

{"title":"TinyML Based Edge Intelligent English Classroom Quality Assessment Scheme","authors":"Shuang Jiang","doi":"10.1002/itl2.70072","DOIUrl":"https://doi.org/10.1002/itl2.70072","url":null,"abstract":"<div>\u0000 \u0000 <p>With the escalating demand for intelligent educational evaluation driven by the advancement of artificial intelligence and edge computing, traditional English classroom quality assessment methods, characterized by subjectivity, inefficiency, and lack of real-time feedback, struggle to meet modern educational needs. Moreover, cloud-based AI solutions pose risks to student data privacy and suffer from high latency. TinyML, a lightweight machine learning paradigm, is inherently compatible with edge intelligence due to its ability to run efficiently on resource-constrained edge devices and perform local inference, thereby reducing latency and safeguarding data privacy. This paper presents an edge-intelligently assisted English classroom quality evaluation scheme based on the TinyML model, which utilizes edge computing devices to analyze classroom interaction data in real time, improving the objectivity and timeliness of teaching quality assessment. The scheme employs lightweight deep learning models for various analyses and conducts localized data processing to avoid cloud-related issues. Experimental results demonstrate its superiority over traditional cloud AI schemes in accuracy, real-time performance, and resource utilization, offering a viable approach for intelligent education evaluation.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573700","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

Investigation Into Dynamic Monitoring and Adjustment of Manufacturing Carbon Emissions Integrating IIoT and 5G 集成工业物联网和5G的制造业碳排放动态监测与调整研究

IF 0.9

Internet Technology Letters Pub Date : 2025-07-07 DOI: 10.1002/itl2.70081

Shu Ying, Han Hang, Peng Shijie

{"title":"Investigation Into Dynamic Monitoring and Adjustment of Manufacturing Carbon Emissions Integrating IIoT and 5G","authors":"Shu Ying, Han Hang, Peng Shijie","doi":"10.1002/itl2.70081","DOIUrl":"https://doi.org/10.1002/itl2.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>The manufacturing industry confronts challenges including inefficient data handling, inadequate real-time monitoring, and vague adjustment mechanisms regarding carbon emission management. Integrating the Industrial Internet of Things (IIoT) and Fifth-generation mobile communication technology (5G) technologies is urgently needed. By constructing a carbon emission sensing network with IIoT-5G converged architecture, a low-latency time-sensitive network (TSN) communication protocol for heterogeneous industrial devices is designed to meet the needs of efficient communication among multiple devices. Combining digital twins and edge computing technology, this study developed a carbon footprint dynamic visualization engine to display carbon emission data in real-time and support carbon emission propagation path modeling based on graph neural network (GNN) to predict and analyze the dynamic changes of carbon emissions accurately. Applying IIoT and 5G technologies has improved monitoring accuracy in a pilot manufacturing site and shown significant energy conservation and emission reduction results. After implementing this technology, the carbon emission intensity decreased from 75.4 to 59.8, and the energy utilization efficiency increased from 32.7% to 90.1%. The waste gas treatment efficiency increased from 16.4% to 34.2%.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573685","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

Behavior Analysis-Assisted Classroom Teaching Based on Multi-Representation Computer Vision Under the Industrial Internet of Things Framework 工业物联网框架下基于多表示计算机视觉的行为分析辅助课堂教学

IF 0.9

Internet Technology Letters Pub Date : 2025-07-05 DOI: 10.1002/itl2.70079

Jiang Hui, Li Yuelong, Zhang Jian

{"title":"Behavior Analysis-Assisted Classroom Teaching Based on Multi-Representation Computer Vision Under the Industrial Internet of Things Framework","authors":"Jiang Hui, Li Yuelong, Zhang Jian","doi":"10.1002/itl2.70079","DOIUrl":"https://doi.org/10.1002/itl2.70079","url":null,"abstract":"<div>\u0000 \u0000 <p>In traditional classroom settings, teachers predominantly rely on visual observation and verbal questioning to assess student status, limiting the ability to deliver timely and precise feedback. To address this limitation, this study introduces a multi-modal computer vision-based behavior analysis approach within an Industrial Internet of Things (IIoT) framework. The proposed system utilizes multiple cameras to capture behavioral indicators—such as speech, facial expressions, and body posture—and integrates deep learning models (e.g., YOLO, SSD) for real-time recognition of students' learning states. By leveraging IIoT's data transmission and edge computing capabilities, the system significantly enhances the accuracy and responsiveness of classroom behavior monitoring. Experimental results indicate that the method effectively detects student attention, engagement, and emotional states, thereby supporting dynamic instructional adjustments. This research contributes to advancing smart education initiatives aligned with Industry 5.0 paradigms.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558208","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

Energy-Efficient Offloading Task in the 5G Edge-Cloud Continuum Using Probabilistic Spiking Networks With Tactical Unit Optimization 基于战术单元优化的概率尖峰网络的5G边缘云连续体节能卸载任务

IF 0.9

Internet Technology Letters Pub Date : 2025-07-03 DOI: 10.1002/itl2.70073

N. Porchelvi, Elamparithi Pandian, P. Prabakaran, Samaya Pillai, R. Dhivya, U. Arun Kumar

{"title":"Energy-Efficient Offloading Task in the 5G Edge-Cloud Continuum Using Probabilistic Spiking Networks With Tactical Unit Optimization","authors":"N. Porchelvi, Elamparithi Pandian, P. Prabakaran, Samaya Pillai, R. Dhivya, U. Arun Kumar","doi":"10.1002/itl2.70073","DOIUrl":"https://doi.org/10.1002/itl2.70073","url":null,"abstract":"<div>\u0000 \u0000 <p>In order to maximize resource usage, minimize latency, and enhance energy efficiency in Mobile Edge Computing (MEC), task offloading is crucial in the 5G Edge-Cloud Continuum. Traditional methods suffer from high computational complexity and static decision-making, leading to inefficiencies. This work proposes a Probabilistic Spiking Neural Network with Tactical Unit Algorithm (PSNN-TUA) for adaptive, low-power task offloading. The system operates across three tiers: Device Layer, MEC Layer, and Cloud Layer, categorizing tasks as delay-sensitive, energy-sensitive, or latency-insensitive. Execution is modeled using Alpha-Beta-Gamma (ABG) path loss and OFDMA-based transmission. The PSNN-based decision model represents UEs, MEC servers, and cloud servers as spiking neurons, using spike probabilities to allocate offloading tasks according to network conditions and resource availability. The decision-making process based on membrane potentials facilitates appropriate work allocation and enhances computing efficiency. The TUA executes three operational stages namely Searchers Action followed by Executors Action then finishes with Assessors Combat Assessment to find optimal PSNN hyperparameters. The experimental data shows PSNN-TUA delivers superior performance compared to other methods by reaching 98.5% MEC availability with 12.3 ms latency and 0.75 J/task energy efficiency alongside 97.2% task completion and 0.9% packet loss and 1.3% failure rate which demonstrates its effectiveness for 5G MEC environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551110","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

The Internet of Things (IoT)-Based Smart Healthcare System (SHS), Using Blockchain Technique 基于物联网（IoT）的智能医疗系统（SHS），使用区块链技术

IF 0.9

Internet Technology Letters Pub Date : 2025-07-01 DOI: 10.1002/itl2.70064

İbrahim Ahmad Yousef Alkhatib, Mutasem Azmi Alkarablieh, Odai Alabadleh, Malek Suliman Alshnaikat, Mahmoud Abushawali, Monther S. Al-atoum, Musab Alqudah, Anas Atef Shamaileh, Bilal A. Salih Ozturk

{"title":"The Internet of Things (IoT)-Based Smart Healthcare System (SHS), Using Blockchain Technique","authors":"İbrahim Ahmad Yousef Alkhatib, Mutasem Azmi Alkarablieh, Odai Alabadleh, Malek Suliman Alshnaikat, Mahmoud Abushawali, Monther S. Al-atoum, Musab Alqudah, Anas Atef Shamaileh, Bilal A. Salih Ozturk","doi":"10.1002/itl2.70064","DOIUrl":"https://doi.org/10.1002/itl2.70064","url":null,"abstract":"<div>\u0000 \u0000 <p>The WSN that has been developed for the Internet of Things (IoT)-based smart healthcare system (SHS) utilizes the 5G and IoT protocols. WSN-assisted IoT systems may be employed for many purposes. The primary criterion for every SHS application is energy efficiency, namely the reduced energy consumption of sensor nodes deployed in the field. In addition to that, SHS applications have crucial requirements for communication latency, security, and QoS performance. The suggested blueprint of an intelligent healthcare system comprises many tiers of Industry 4.0 (IoT) standards, including the edge layer, fog layer, and storage layer. The edge layer comprises a set of nodes that gather the patient's periodic information through various body sensors. The nodes that are collared red represent the patients that are wearing the body sensors. The medical data acquired at the edge layer is wirelessly sent to the fog nodes located at the fog layer. The fog node collects the information gathered by the edge devices in its immediate vicinity. Fog nodes encompass a variety of network devices such as routers, access points, gateways, and base stations. Ultimately, the storage layer is responsible for receiving data from the fog nodes to store and analyze it. Cloud storage services are utilized by several applications to access, analyze, and make decisions. As previously said, while creating and implementing a blockchain-based healthcare system, it is important to solve the problems associated with the following terms: The data Storage: Given the extensive network of medical patients and hospitals connected by EHRs, it is imperative to employ a computationally efficient and robust cryptographic technique to establish the blockchain framework.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519756","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

Context-Aware Satellite Remote Sensing Fire Point Detection Based on Energy Scores 基于能量分数的情境感知卫星遥感火点探测

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Internet Technology Letters Pub Date : 2025-06-29 DOI: 10.1002/itl2.70066

Tao Feng, Huayu Zhang, Yi Ouyang

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Improving Underwater Image Quality Through Real-ESRGAN With Whale Optimization Algorithm 基于鲸鱼优化算法的Real-ESRGAN提高水下图像质量

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Internet Technology Letters Pub Date : 2025-06-29 DOI: 10.1002/itl2.70047

Priyanka Nandal, Prerna Mann, Navdeep Bohra, Kalpna Sagar, Aseel Smerat

{"title":"Improving Underwater Image Quality Through Real-ESRGAN With Whale Optimization Algorithm","authors":"Priyanka Nandal, Prerna Mann, Navdeep Bohra, Kalpna Sagar, Aseel Smerat","doi":"10.1002/itl2.70047","DOIUrl":"https://doi.org/10.1002/itl2.70047","url":null,"abstract":"<div>\u0000 \u0000 <p>Unique optical properties of underwater environments, like low resolution, blurriness, and color distortion, are common challenges for underwater imaging. Consequently, the imaging equipment suffers from water turbidity, light attenuation, and scattering in aquatic environments, despite the improvement in hardware, resulting in lesser-quality, distorted, and poorly contrasted color images. An innovative approach to enhance underwater images by integrating Real-ESRGAN (Real-Enhanced Super-Resolution Generative Adversarial Network) with a Whale Optimization Algorithm (WOA) is studied in this research to address these issues. To fine-tune the model parameters and improve the overall image enhancement process, Real-ESRGAN, known for its superior performance in quality image resolution enhancement, is combined with WOA, a nature-inspired optimization algorithm. Extensive experiments on the LSUI dataset are conducted to evaluate the efficacy of this approach. The efficacy of the suggested approach is assessed comprehensively, combining qualitative visual analysis with quantitative metrics. The proposed method demonstrates strong quantitative performance, achieving a PSNR of 35.48, SSIM of 0.82, UIQM of 4.60, RMSE of 0.25, and entropy of 5.50. The outcomes indicate notable upgradation in image clarity, detail, and color accuracy compared to existing enhancement techniques. This research contributes to underwater imaging by offering an innovative solution that enhances the quality of underwater visuals.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514591","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

Future Directions in Multiple Access for 6G: Emerging Paradigms and Insights 6G多址接入的未来方向：新兴范式和见解

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Internet Technology Letters Pub Date : 2025-06-29 DOI: 10.1002/itl2.70067

Saumya Chaturvedi, Vivek Ashok Bohara

引用次数: 0

Intelligent Music Streaming Scheduling and QoE Optimization in 6G Wireless Networks Using Large-Scale Models 基于大规模模型的6G无线网络智能音乐流调度和QoE优化

IF 0.9

Internet Technology Letters Pub Date : 2025-06-26 DOI: 10.1002/itl2.70062

Xudong Qiao

{"title":"Intelligent Music Streaming Scheduling and QoE Optimization in 6G Wireless Networks Using Large-Scale Models","authors":"Xudong Qiao","doi":"10.1002/itl2.70062","DOIUrl":"https://doi.org/10.1002/itl2.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we propose LM-QoEStream, a novel framework that integrates large-scale language models (LLMs) with reinforcement learning-based streaming scheduling to optimize music delivery under dynamic wireless conditions. Specifically, we design a prompt-driven Quality of Experience (QoE) prediction module that transforms heterogeneous user, content, and network features into structured natural language prompts, enabling the LLM to infer fine-grained user satisfaction scores. These scores are then used as rewards in a Soft Actor-Critic (SAC) reinforcement learning (RL) controller that dynamically adjusts streaming parameters such as bitrate and buffer strategies. Extensive experiments conducted on simulated 5G/6G networks with real-world content and user interaction traces demonstrate that LM-QoEStream significantly outperforms baseline methods in terms of average QoE, stall ratio, bitrate adaptation accuracy, and fairness. Ablation studies further confirm the complementary strengths of both the LLM-based perception model and the learning-based decision module. The proposed approach offers a scalable, generalizable, and user-centric solution for next-generation music streaming systems.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492678","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