Internet Technology Letters最新文献_第2页

SafeEdge: Intention-Aware Cooperative Motion Planning for Autonomous Vehicles Over Mobile Edge Networks SafeEdge：移动边缘网络上自动驾驶汽车的意图感知协同运动规划

IF 0.5

Internet Technology Letters Pub Date : 2025-10-02 DOI: 10.1002/itl2.70143

Jindan Zhao

{"title":"SafeEdge: Intention-Aware Cooperative Motion Planning for Autonomous Vehicles Over Mobile Edge Networks","authors":"Jindan Zhao","doi":"10.1002/itl2.70143","DOIUrl":"https://doi.org/10.1002/itl2.70143","url":null,"abstract":"<div>\u0000 \u0000 <p>Autonomous vehicles often struggle in dense urban intersections because of occlusions and reactive single-vehicle planners. SafeEdge tackles this challenge by partitioning cooperative motion planning across a three-tier mobile-edge hierarchy. A graph-transformer intention generator running on roadside and metro-edge nodes fuses V2X trajectory snippets from up to 120 agents into kilobyte-size probability maps of future maneuvers. Each vehicle keeps feasibility checks on board, while large-scale collision resolution is off-loaded to a metro-edge mixed-integer solver. A Coq-verified safety shield triggers emergency braking whenever network latency exceeds a derived bound. Deployed on 10 Jetson Orin NX cars and 4 Xeon Silver edge servers over a standalone 5 G link, SafeEdge clears four-way intersections with a 96% success rate and a 95th-percentile decision latency of 32 ms—well below the 50 ms safety envelope. Relative to an on-board MPC baseline, emergency-brake events drop by 70% and energy per kilometer falls by 11%. These results demonstrate that intention-aware, edge-partitioned planning can simultaneously satisfy real-time and safety requirements in dense urban driving.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223845","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

Enhanced RoBERTaSN Model for Industrial IoT Text Similarity Analysis in Smart Manufacturing Systems 智能制造系统中工业物联网文本相似度分析的增强RoBERTaSN模型

IF 0.5

Internet Technology Letters Pub Date : 2025-10-02 DOI: 10.1002/itl2.70155

Maochun Xu, Qiang Liu, Gang Li, Chengmeng Li, Lei Ma, Ke Lin

{"title":"Enhanced RoBERTaSN Model for Industrial IoT Text Similarity Analysis in Smart Manufacturing Systems","authors":"Maochun Xu, Qiang Liu, Gang Li, Chengmeng Li, Lei Ma, Ke Lin","doi":"10.1002/itl2.70155","DOIUrl":"https://doi.org/10.1002/itl2.70155","url":null,"abstract":"<div>\u0000 \u0000 <p>In Industrial Internet of Things (IIoT) environments, smart manufacturing systems generate massive textual data (equipment logs, maintenance reports, etc.) requiring accurate similarity analysis for fault diagnosis and predictive maintenance. Traditional methods underperform in Industry 5.0 scenarios due to technical vocabulary and domain-specific language. This paper presents RoBERTaSN, an enhanced model combining RoBERTa with a Siamese network, featuring self-attention and dual pooling optimized for industrial texts. It enables precise similarity calculations between fault descriptions and historical records. Experiments on industrial datasets (e.g., equipment fault logs, maintenance reports) yield 94.2% accuracy in fault diagnosis text matching—7.8% higher than traditional TF-IDF (86.4%) and 6.0% higher than mainstream pretrained models (BERT: 88.2% accuracy; BiMPM: 84.67% <i>F</i>1-score), addressing semantic challenges in smart factories and advancing Industry 5.0's human–machine collaboration and intelligent decision-making goals.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223847","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 English Text Recognition Using Lightweight AI in Wireless Communication Networks 基于轻量级AI的无线通信网络实时英语文本识别

IF 0.5

Internet Technology Letters Pub Date : 2025-09-30 DOI: 10.1002/itl2.70125

Baoying Sun, Yingwei Liu

{"title":"Real-Time English Text Recognition Using Lightweight AI in Wireless Communication Networks","authors":"Baoying Sun, Yingwei Liu","doi":"10.1002/itl2.70125","DOIUrl":"https://doi.org/10.1002/itl2.70125","url":null,"abstract":"<div>\u0000 \u0000 <p>In the era of pervasive wireless communication, the need for efficient and accurate text recognition systems is growing, especially for applications involving edge devices in resource-constrained environments. This paper proposes a lightweight AI-based approach for English text recognition, leveraging a hybrid model combining convolutional neural networks (CNN) and gated recurrent units (GRU). The model effectively handles noisy wireless signals by capturing both spatial and temporal features from modulated signals. We incorporate techniques such as pruning and depthwise separable convolution (DSC) to reduce the model's size, making it suitable for deployment in wireless communication systems. Experimental results demonstrate that the proposed model outperforms several state-of-the-art methods, including traditional modulation recognition and deep learning-based alternatives, in terms of both recognition accuracy and model efficiency, even in low signal-to-noise ratio (SNR) conditions. The proposed model offers a promising solution for real-time text recognition in wireless communication environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224563","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

IIoT-Enabled Apparel Demand Forecasting: A Random Forest Approach Mining E-Commerce Reviews 基于工业物联网的服装需求预测：随机森林方法挖掘电子商务评论

IF 0.5

Internet Technology Letters Pub Date : 2025-09-30 DOI: 10.1002/itl2.70158

Zhihang Tang, Jinyang Shi, Zipei Tang

{"title":"IIoT-Enabled Apparel Demand Forecasting: A Random Forest Approach Mining E-Commerce Reviews","authors":"Zhihang Tang, Jinyang Shi, Zipei Tang","doi":"10.1002/itl2.70158","DOIUrl":"https://doi.org/10.1002/itl2.70158","url":null,"abstract":"<div>\u0000 \u0000 <p>Within the industrial internet of things (IIoT) ecosystems, apparel manufacturers face the dual challenge of integrating high-velocity consumer feedback streams from e-commerce platforms and translating them into real-time, high-fidelity demand forecasts. This study presents an IIoT-native framework that employs random forest regression (RFR) to fuse multi-modal review features—sentiment polarity, key phrases, and aggregate ratings—collected via edge gateways from 1100 men's garments on \u0000JD.com. Innovatively, the proposed framework not only outperforms traditional linear models such as ordinary least squares (OLS) and multiple linear regression (MLR) in terms of predictive accuracy but also demonstrates robustness to noise and outliers across heterogeneous product categories. The cloud-hosted RFR model achieves an <i>R</i><sup>2</sup> of 0.9442 and root mean square error (RMSE) of 105.76, representing a 5.6% and 35.9% improvement over MLR and OLS in RMSE, respectively. This study provides the first multi-category empirical evidence that fusing review-level sentiment, key phrases, and ratings via RFR yields significant enhancements in IIoT-scale apparel demand forecasting.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224465","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 Digital Twin Networks for Transmission Optimization in 5G/6G Wireless Communication Systems 面向5G/6G无线通信系统传输优化的大型模型驱动数字孪生网络

IF 0.5

Internet Technology Letters Pub Date : 2025-09-26 DOI: 10.1002/itl2.70113

Ankita Sharma, Shalli Rani

{"title":"Large Model-Driven Digital Twin Networks for Transmission Optimization in 5G/6G Wireless Communication Systems","authors":"Ankita Sharma, Shalli Rani","doi":"10.1002/itl2.70113","DOIUrl":"https://doi.org/10.1002/itl2.70113","url":null,"abstract":"<div>\u0000 \u0000 <p>The next generation of Wireless Communication Networks (WCNs), such as 5G and 6G, require highly adaptive, intelligent, and efficient transmission mechanisms to meet the demands of low latency, high throughput, and robust Quality of Experience (QoE). This paper introduces a novel framework that integrates Large Models (LMs), particularly transformer-based deep learning architectures, with Digital Twin Networks (DTNs) for predictive and real-time optimization in WCNs. The proposed LM-enhanced DTN architecture enables advanced capabilities such as traffic classification, predictive scheduling, quality-aware transmission, and failure forecasting. Experimental evaluations using real-world telemetry datasets demonstrate the superiority of the LM-powered system in achieving over 98% classification accuracy and enhancing 12.4% improvement in QoE in congested scenarios. Additionally, a case study in industrial networks illustrates the effectiveness of this approach in predictive maintenance and adaptive traffic management. This work paves the way for self-optimizing, intelligent wireless networks by harnessing the cognitive power of large AI models in virtual network replicas.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146442","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

Design and Implementation of Intelligent Construction Automation System Based on 6G Network 基于6G网络的智能建筑自动化系统的设计与实现

IF 0.5

Internet Technology Letters Pub Date : 2025-09-26 DOI: 10.1002/itl2.70045

Xiujun Nie, Xiaolin Zhang, Xuguo Liu, Ran Wang

{"title":"Design and Implementation of Intelligent Construction Automation System Based on 6G Network","authors":"Xiujun Nie, Xiaolin Zhang, Xuguo Liu, Ran Wang","doi":"10.1002/itl2.70045","DOIUrl":"https://doi.org/10.1002/itl2.70045","url":null,"abstract":"<div>\u0000 \u0000 <p>In modern intelligent construction automation systems, due to the interference of network delay, task synchronization between devices is hindered, resulting in uncoordinated operations between robots and collisions, and task conflicts. This paper builds an intelligent construction automation system based on a 6G network, using the low latency and high bandwidth characteristics of a 6G network to effectively solve the delay problem in task synchronization and collaborative work. Its innovative combination of network slicing technology and edge computing methods customizes specific network resources for different application scenarios to minimize latency. The fusion of convolutional neural network (CNN) and long short-term memory (LSTM) models can make better predictions, and combined with the deep reinforcement learning model (DRL), a path planning plan can be formulated based on the prediction results to avoid collision problems in the robot's work. The experimental results show that after the 6G network optimization system, the task scheduling rate of the robot can reach 0.95, compared with 5G network optimization, which only reaches 0.90, and the collision problem of the robot can be well avoided. The collision rate after optimization can approach 0, which can ensure the smooth progress of the construction process and the safety and reliability of task execution.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146443","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 Hybrid Network Speech Recognition Method for English Short Passage Reading Emotion Analysis in Multi-Access Edge Intelligence Scenarios 多访问边缘智能场景下英语短文阅读情感分析的混合网络语音识别方法

IF 0.5

Internet Technology Letters Pub Date : 2025-09-26 DOI: 10.1002/itl2.70108

Jun Liao

引用次数: 0

Queue-Aware Congestion Avoidance in IoHT: Enabling Future Integration With Large Models for Transmission Optimization IoHT中的队列感知拥塞避免：实现未来与传输优化大型模型的集成

IF 0.5

Internet Technology Letters Pub Date : 2025-09-25 DOI: 10.1002/itl2.70136

Muhammad Zafarullah, Ata Ullah, Fazli Subhan, Sajjad A. Ghauri, Mazliham Mohd Suud, M. Mansoor Alam

{"title":"Queue-Aware Congestion Avoidance in IoHT: Enabling Future Integration With Large Models for Transmission Optimization","authors":"Muhammad Zafarullah, Ata Ullah, Fazli Subhan, Sajjad A. Ghauri, Mazliham Mohd Suud, M. Mansoor Alam","doi":"10.1002/itl2.70136","DOIUrl":"https://doi.org/10.1002/itl2.70136","url":null,"abstract":"<p>The internet of healthcare things (IoHT) has advanced considerably, improving healthcare operations and patient monitoring by continuously collecting data from health sensors attached to patients. Current congestion detection techniques are insufficient for early detection since senders often remain unaware of the size of the residual queue. The real-time transmission of critical health data is essential, yet frequent congestion at intermediate nodes can lead to increased packet loss, delays, and diminished system reliability. To tackle these challenges, we propose a robust and low-complexity QACA algorithm tailored specifically for patient-centric IoHT networks, which dynamically adjusts the frequency of acknowledgments based on real-time queue occupancy thresholds. By integrating interval-based acknowledgments with a priority-based queuing strategy, QACA ensures that high-priority medical data is transmitted promptly, even in the face of heavy network loads. Simulation results indicate that QACA significantly improves performance over the analytical model and DCCA regarding packet loss and packet delay reduction. Moreover, the current framework may be enhanced in future work with the use of LMs to add predictive estimation of queue status, traffic classification, and an intelligent transmission scheduling, thus paving the way toward scalable and intelligent congestion management in next-generation IoHT systems.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.70136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large Model-Based Multi-Community Virtual Interaction Scheme in Wireless Networks 无线网络中基于大模型的多社区虚拟交互方案

IF 0.5

Internet Technology Letters Pub Date : 2025-09-23 DOI: 10.1002/itl2.70147

Liping Zhang

{"title":"Large Model-Based Multi-Community Virtual Interaction Scheme in Wireless Networks","authors":"Liping Zhang","doi":"10.1002/itl2.70147","DOIUrl":"https://doi.org/10.1002/itl2.70147","url":null,"abstract":"<div>\u0000 \u0000 <p>The increasing ubiquity of 5G/6G wireless communication networks has created unprecedented opportunities for multi-community collaboration and virtual engagement. However, existing platforms often fail to support scalable, context-aware, and efficient interaction across decentralized social groups. In this paper, we propose LM-MCVIS (Large Model-Based Multi-Community Virtual Interaction Scheme), a novel framework designed to facilitate personalized content exchange and context-aware message routing in wireless community networks. LM-MCVIS integrates three key components: (1) an Edge-Aware Prompt Compression (EPC) module that semantically distills conversation inputs to reduce wireless transmission overhead; (2) a Community State Encoder (CSE) that models dynamic group structures and latent social contexts; and (3) a Federated Reinforcement Optimizer (FRO) that enables privacy-preserving, feedback-driven content routing. We evaluate LM-MCVIS on three public multi-community datasets using a high-fidelity 5G/6G emulator and benchmark its performance against five strong baselines. Results demonstrate significant gains in engagement depth, interaction diversity, response latency, and bandwidth savings. Ablation studies further validate the individual impact of each module. LM-MCVIS offers a scalable and modular paradigm for intelligent community interaction over wireless networks, with broad implications for collaborative learning, digital governance, and decentralized social ecosystems.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146321","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

Review of 6G Wireless Communication System With Artificial Intelligence 基于人工智能的6G无线通信系统综述

IF 0.5

Internet Technology Letters Pub Date : 2025-09-22 DOI: 10.1002/itl2.70127

Suman Turpati, B. Geetha Rani, A. V. Prabu, Amrit Mukherjee, Sudan Jha, K. C. T. Swamy

{"title":"Review of 6G Wireless Communication System With Artificial Intelligence","authors":"Suman Turpati, B. Geetha Rani, A. V. Prabu, Amrit Mukherjee, Sudan Jha, K. C. T. Swamy","doi":"10.1002/itl2.70127","DOIUrl":"https://doi.org/10.1002/itl2.70127","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless communication has been in high demand over the last decades. Soon, the globe will be equipped with fifth-generation (5G) communications, which provide an incredible number of additional capabilities compared to fourth-generation communications. An innovative paradigm has evolved with the combination of artificial intelligence (AI) with sixth-generation (6G) communication networks in response to the increasing need for intelligent communication and seamless connection. This integration enables optimum resource allocation and greater efficiency. It also enhances adaptive system performance by incorporating AI across multiple network layers. The next generation of wireless networks must address many fundamental issues, including increasing system capacity, data throughput, latency, security, and quality of service in comparison to 5G. This article provides a through review of the vision of future 6G network AI and wireless communication architecture, touching on their conceptual foundations, inherent difficulties, and potential fields for further study. Some new technologies discussed in this article include AI, terahertz communications, free-space optical networks, blockchain, quantum communications, drones, mobile free communications, integrated sensing and communication, dynamic network slicing, big data analytics, and wireless optical technology. This could all be useful in ensuring the quality of service in the 6G architecture development. Furthermore, we provide a concise overview of the AI standardization process for wireless networks, focusing on essential achievements and current initiatives. We also examine the significant obstacles that 6G's AI and communication integration encountered. Lastly, we provide an overview of prospective future studies that ideally promote advancing and improving AI and 6G communications by describing possible obstacles and possibilities.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110802","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