Internet Technology Letters最新文献_第5页

UAV-Assisted C-RAN for On-Demand Cellular Coverage: Opportunities and Challenges 无人机辅助C-RAN按需蜂窝覆盖：机遇与挑战

IF 0.5

Internet Technology Letters Pub Date : 2025-09-01 DOI: 10.1002/itl2.70117

Byomakesh Mahapatra, Deepika Gupta, Pankaj Kumar Sharma

{"title":"UAV-Assisted C-RAN for On-Demand Cellular Coverage: Opportunities and Challenges","authors":"Byomakesh Mahapatra, Deepika Gupta, Pankaj Kumar Sharma","doi":"10.1002/itl2.70117","DOIUrl":"https://doi.org/10.1002/itl2.70117","url":null,"abstract":"<div>\u0000 \u0000 <p>The deployment of beyond fifth-generation (5G) infrastructure over disaster-affected regions, temporary hotspot situations (e.g., massive gatherings, etc.), and complex terrains (e.g., sea, hills, marshes, etc.) poses numerous challenges for cellular service providers. Recently, unmanned aerial vehicles (UAVs) have emerged as potential candidates to overcome the aforementioned technical issues based on their multi-role capabilities to serve as aerial base stations, mobile relays, and flying wireless access points. As such, the UAVs can act as portable platforms that can be deployed immediately on demand without requiring massive ground infrastructure to support wireless services. This article introduces the integration of UAVs to cloud radio access networks (C-RAN) for beyond 5G applications. Despite various advantages, limitations such as limited power backup, delicate hardware, and restricted payload make UAVs unsuitable for large-scale operations such as macro-base station. The article mainly focuses on the underlying opportunities and challenges to realize the UAV-assisted C-RAN (UC-RAN) architecture in view of three generic application scenarios, i.e., disaster management, hotspots, and complex terrains. A preliminary performance analysis via simulation is provided for the proposed UC-RAN architecture under the hotspot application scenario based on the relevant metrics.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927505","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-Friendly NAS Framework for Mural Pattern Recognition via Structure-Aware Feature Fusion 基于结构感知特征融合的壁画模式识别边缘友好NAS框架

IF 0.5

Internet Technology Letters Pub Date : 2025-08-31 DOI: 10.1002/itl2.70109

Xianke Zhou, Wenjie Deng, Fengran Xie

引用次数: 0

IoT-Enabled Remote Teaching Management and Interaction: A Smart Framework for Real-Time Engagement and Environment Optimization 支持物联网的远程教学管理和互动：实时参与和环境优化的智能框架

IF 0.5

Internet Technology Letters Pub Date : 2025-08-31 DOI: 10.1002/itl2.70123

Shanshan Wang

{"title":"IoT-Enabled Remote Teaching Management and Interaction: A Smart Framework for Real-Time Engagement and Environment Optimization","authors":"Shanshan Wang","doi":"10.1002/itl2.70123","DOIUrl":"https://doi.org/10.1002/itl2.70123","url":null,"abstract":"<div>\u0000 \u0000 <p>The shift to remote teaching has accelerated the demand for intelligent systems that can sustain high engagement and manage virtual classroom environments effectively. This paper proposes an IoT-enabled framework for remote teaching management and interaction that integrates environmental control, behavioral sensing, and real-time feedback mechanisms. The system adopts a three-tier architecture comprisinga perception layer with IoT sensors and edge computing nodes for data collection and preprocessing, a network layer that manages secure communication via the MQTT protocol, and an application layer offering cloud-based analytics, PID control, and user interfaces. This architecture enables precise regulation of temperature and lighting through PID control, as well as real-time tracking of student engagement using multimodal sensing and scoring algorithms. Extensive experiments involving 100 students and six comparison methods demonstrate the superiority of the proposed system in terms of engagement score, environmental stability (RMSE), delay, and instructor satisfaction. Quantitative metrics and visual analyses reveal that our solution reduces average data transmission latency to 21.4 ms and increases engagement by 12% over existing smart classroom models. These findings underscore the potential of IoT-driven intelligent frameworks in enhancing the interactivity, efficiency, and comfort of remote learning environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923455","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

AI-Driven Secure 5G MIMO Enhancing Robotic Precision in Industrial and Service Applications 人工智能驱动的安全5G MIMO提高工业和服务应用中的机器人精度

IF 0.5

Internet Technology Letters Pub Date : 2025-08-27 DOI: 10.1002/itl2.70077

Yuanfang Wei

{"title":"AI-Driven Secure 5G MIMO Enhancing Robotic Precision in Industrial and Service Applications","authors":"Yuanfang Wei","doi":"10.1002/itl2.70077","DOIUrl":"https://doi.org/10.1002/itl2.70077","url":null,"abstract":"<div>\u0000 \u0000 <p>The incorporation of AI in conjunction with secure 5G MIMO networks enhances the precision of consumer and industrial robotics. Ultra-reliable, low-latency communication paired with autonomous control enables faster, safer, and more accurate action execution in dynamic environments. However, contemporary robotic communication systems face challenges such as being highly susceptible to signal interference, network delays, cyber-attacks, and lack of adaptive capability. These obstacles particularly hinder remote control teleoperation and robotic efficiency in conditions which are highly volatile or constantly changing. The framework proposed, AI-Driven Secure 5G MIMO for Robotic Precision (AI-5G-MIMO-RP), uses AI adaptive signal processing to manage assistive cyber defense systems and strong 5G MIMO communications to overcome such challenges. MIMO technology not only increases data transmission speed, but also enhances dependability, while machine learning helps optimize data routing within the signals. AI-fortified cyber defenses detect and mitigate real-time and pre-emptive breaches, ensuring system communications cannot be tampered with. This approach supports application areas with smart precision like manufacturing, robotics for healthcare, facilitating automation in remote assistance, and serving in automated logistics. This technology enables dependably safe control and low-latency communication, guaranteeing accurate robot operation in complex tasks without human oversight. AI-5G-MIMO-RP creates a new standard in precision robotics control, network resilience, and operational efficiency. This technology reduces communication delays, increases network flexibility, and enhances system reliability, making industrial and service settings safer and more efficient than previous systems.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905454","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

Dynamic eNodeB Antenna Tilting for Enhanced Efficiency in Self-Organizing Networks 动态eNodeB天线倾斜提高自组织网络效率

IF 0.5

Internet Technology Letters Pub Date : 2025-08-27 DOI: 10.1002/itl2.70116

B. N. Patil, Nipun Setia, R. Murugan, Dheeraj Kumar Singh, Ashmeet Kaur, Amit Kansal

{"title":"Dynamic eNodeB Antenna Tilting for Enhanced Efficiency in Self-Organizing Networks","authors":"B. N. Patil, Nipun Setia, R. Murugan, Dheeraj Kumar Singh, Ashmeet Kaur, Amit Kansal","doi":"10.1002/itl2.70116","DOIUrl":"https://doi.org/10.1002/itl2.70116","url":null,"abstract":"<div>\u0000 \u0000 <p>Dynamic evolved NodeB (eNB) antenna tilting, base station antenna angles are adjusted to optimize signal quality and range based on community conditions. It proposes Energy Efficiency (EE) solutions for microcells based on a variable eNB antenna tilt configuration for self-organizing networks (SONs) for network scenarios. This is a crucial part of minimal latency and fast speeds in wireless network mobility management. Utilizing a distributed mechanism, the SON architecture provides the ability to share network information with neighboring cells and the overall network. Modifications in eNB antenna tilt are commonly employed in wireless networks to prevent interference and increase cell coverage since the antenna's emissions pattern is directly influenced by angle. Research used a Learning curve-driven deep deterministic policy gradient (LC-DDPG) for eNB antenna tilt optimization. Simulations indicate that LC-DDPG performs better. The approach also meets SON's needs for agility and scalability. However, factors such as terrain uncertainty and interference may limit its performance and hinder the best potential signal transmission.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910208","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

Content Delivery Network Driven Audio Recognition for Enhancing English Interaction Scenarios 内容交付网络驱动的音频识别增强英语交互场景

IF 0.5

Internet Technology Letters Pub Date : 2025-08-27 DOI: 10.1002/itl2.70105

Liyuan Teng

{"title":"Content Delivery Network Driven Audio Recognition for Enhancing English Interaction Scenarios","authors":"Liyuan Teng","doi":"10.1002/itl2.70105","DOIUrl":"https://doi.org/10.1002/itl2.70105","url":null,"abstract":"<div>\u0000 \u0000 <p>The growing demand for real-time multilingual speech interaction systems poses significant challenges in terms of latency, scalability, and contextual accuracy. Conventional cloud-based solutions suffer from high delays, while edge devices lack computational resources for complex models. Static CDN configurations further exacerbate regional resource underutilization, and existing systems achieve relatively lower intent accuracy in multilingual scenarios. To address these limitations, we propose a three-tier framework integrating predictive CDN and a lightweight tiny machine learning-based audio recognition on which the multi-attention is introduced for context-aware English interaction scenarios. In particular, the LSTM model is leveraged to implement the on-device fine-tuning and context-aware so as to achieve well interactions. The experimental results demonstrate that TinyLSTM achieves superior performance with an error rate of 14.2% and an intent accuracy of 89.7%, while maintaining the lowest latency at 23 ms, making it highly effective for real-time applications on edge devices. Additionally, incorporating quantization, knowledge graphs, and emotion feedback progressively improves model effectiveness and increases engagement scores to 4.9, highlighting the importance of these components in enhancing both technical accuracy and user interaction quality.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910043","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

Large Model-Driven Real-Time Object Detection in High-Definition Video Surveillance Over Wireless Networks 无线网络高清视频监控中的大模型驱动实时目标检测

IF 0.5

Internet Technology Letters Pub Date : 2025-08-26 DOI: 10.1002/itl2.70121

Haihong Guo, Dingding Zhang, Yang Wang, Junfang Tian

{"title":"Large Model-Driven Real-Time Object Detection in High-Definition Video Surveillance Over Wireless Networks","authors":"Haihong Guo, Dingding Zhang, Yang Wang, Junfang Tian","doi":"10.1002/itl2.70121","DOIUrl":"https://doi.org/10.1002/itl2.70121","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes LMDOF-SIOGAN, a novel large model-driven object detection framework that integrates a Swarm Intelligence-Optimized Generative Adversarial Network (SIOGAN) with an adaptive large model detection pipeline. The proposed framework introduces two key modules: (1) a VE-GAN-based visual enhancement module (VE-GAN), which leverages adversarial learning and perceptual supervision to restore semantic integrity from degraded video frames; and (2) a Swarm Intelligence-Aided Scheduler (SIAS), which dynamically optimizes the detection pipeline based on real-time network conditions and video quality assessments. Extensive experiments were conducted on public datasets VisDrone2021 and UAVDT, under simulated wireless video surveillance environments with multi-resolution streams and comprehensive network impairments. The results demonstrate that LMDOF-SIOGAN consistently outperforms state-of-the-art baselines including YOLOv8 and ApproxDet, achieving up to 80.3% [email protected] at 4 K resolution with 125 ms end-to-end latency, while maintaining superior robustness index (RI) and moderate computational overhead. Ablation studies further validate the critical contributions of the VE-GAN and SIAS modules to detection accuracy, robustness, and latency.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897730","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

Real-Time Sports Event Multi-View Streaming Optimization With Large Models: A Collaborative Edge Framework in 5G/6G Wireless Networks 基于大模型的实时体育赛事多视点流优化：5G/6G无线网络中的协作边缘框架

IF 0.5

Internet Technology Letters Pub Date : 2025-08-26 DOI: 10.1002/itl2.70122

Fa Zhang

{"title":"Real-Time Sports Event Multi-View Streaming Optimization With Large Models: A Collaborative Edge Framework in 5G/6G Wireless Networks","authors":"Fa Zhang","doi":"10.1002/itl2.70122","DOIUrl":"https://doi.org/10.1002/itl2.70122","url":null,"abstract":"<div>\u0000 \u0000 <p>Real-time multi-view sports streaming poses challenges in latency, Quality of Experience (QoE), and bandwidth efficiency under dynamic wireless conditions. Traditional centralized methods struggle to meet the demands of personalized viewing in 5G/6G environments. This paper presents CEFLM, a Collaborative Edge Framework empowered by Large Models, which integrates a transformer-based predictor for user viewpoints, a QoE-aware stream selector, and a federated multi-agent scheduler across edge nodes. A cross-layer optimization module further refines video quality and resource allocation. To evaluate CEFLM, we construct two datasets—MVSports-360 with synchronized multi-view annotations and YouTube MV-Highlights with aligned sports highlights. Experimental results show CEFLM achieves a Top-1 viewpoint accuracy of 84.6%, reduces latency by 24%, and improves QoE by 10% over strong baselines. Compared to a recent RL-based method, CEFLM increases QoE by 9.8% and lowers rebuffering. Ablation studies confirm that removing the large model or edge collaboration degrades performance, with QoE dropping by up to 7.9%. These results validate the effectiveness of CEFLM in enhancing adaptive, user-centric multimedia delivery in future wireless networks.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897731","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

Real-Time Monitoring and Data Analysis System of Sports Equipment Based on Industrial Internet of Things 基于工业物联网的运动器材实时监控与数据分析系统

IF 0.5

Internet Technology Letters Pub Date : 2025-08-21 DOI: 10.1002/itl2.70102

Ying Chen, Xiangping Zheng

{"title":"Real-Time Monitoring and Data Analysis System of Sports Equipment Based on Industrial Internet of Things","authors":"Ying Chen, Xiangping Zheng","doi":"10.1002/itl2.70102","DOIUrl":"https://doi.org/10.1002/itl2.70102","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid advancement of the Industrial Internet of Things (IIoT) has revolutionized sports health management by enabling real-time monitoring of athletes' physiological and kinematic data. However, existing systems face challenges in real-time performance, accuracy, and noise resilience during data acquisition and analysis. This study aims to develop a real-time monitoring and data analysis system for sports equipment by integrating wavelet transform and an improved K-nearest neighbors (KNN) algorithm to enhance classification accuracy and system responsiveness. The proposed system employs wavelet transform for multi-resolution noise reduction and feature extraction from sensor data (e.g., acceleration, heart rate, gyroscope). An enhanced KNN algorithm dynamically adjusts feature weights and distance metrics to optimize classification. Experiments were conducted on running, cycling, and weightlifting activities using Raspberry Pi 4 edge devices and wireless sensors (LoRa/ZigBee). Compared to traditional KNN (83% accuracy), the proposed method achieves 89% accuracy, an improvement of 6 percentage points, and reduces system response time from 200 ms to 50 ms (a 75% improvement). This work demonstrates a robust IIoT-based framework for intelligent sports analytics, offering high-precision, low-latency monitoring applicable to training optimization, injury prevention, and personalized health management.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881442","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

Neural Network Modeling Based on Multimodal IIoT Sensing Data: Psychological Stress Assessment and Industrial Human-Machine Collaboration Early Warning System for University Students 基于多模态IIoT传感数据的神经网络建模：大学生心理压力评估与工业人机协作预警系统

IF 0.5

Internet Technology Letters Pub Date : 2025-08-15 DOI: 10.1002/itl2.70093

Chen Shao Hong, Zhong Chun

{"title":"Neural Network Modeling Based on Multimodal IIoT Sensing Data: Psychological Stress Assessment and Industrial Human-Machine Collaboration Early Warning System for University Students","authors":"Chen Shao Hong, Zhong Chun","doi":"10.1002/itl2.70093","DOIUrl":"https://doi.org/10.1002/itl2.70093","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid development of industrial Internet of Things (IIoT), its educational applications extend from equipment monitoring to mental health management. Addressing the limitations of traditional methods (e.g., subjective self-assessment scales) in real-time psychological stress evaluation, this paper proposes a neural network integrating multimodal IIoT data—physiological signals (EEG, HRV), behavioral data (expression, gesture), and interaction logs (text, clickstream)—to build a dynamic fusion and lightweight warning system. The model employs a cross-modal attention mechanism to adaptively allocate data weights (e.g., prioritizing EEG signals by 58% in exam scenarios) and a tensor fusion network (TFN) for feature extraction. An edge-cloud collaborative framework based on federated learning enhances generalization while ensuring privacy through AES-256 encryption, local feature preprocessing, and differential privacy protections during model updates. Experiments on a campus dataset show 89.2% stress classification accuracy (10.7% higher than unimodal approaches), sub-105 ms alert latency, and a 4.7% false alarm rate. Personalized interventions (e.g., counseling) improved stress alleviation by 61.7% for moderate-to-severe cases. This study advances IIoT's role in intelligent mental health management and adaptive human-computer interaction systems.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853774","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