Internet Technology Letters最新文献

Energy-Aware Cross-Layer Routing Using Transformer Models in Wireless Sensor Networks 无线传感器网络中基于变压器模型的能量感知跨层路由

IF 0.5

Internet Technology Letters Pub Date : 2025-10-09 DOI: 10.1002/itl2.70146

Shashi Tanwar, Abdul Lateef Haroon Phulara Shaik, M. Vasantha Kumara, Afshan Kaleem, S. Ranganatha

{"title":"Energy-Aware Cross-Layer Routing Using Transformer Models in Wireless Sensor Networks","authors":"Shashi Tanwar, Abdul Lateef Haroon Phulara Shaik, M. Vasantha Kumara, Afshan Kaleem, S. Ranganatha","doi":"10.1002/itl2.70146","DOIUrl":"https://doi.org/10.1002/itl2.70146","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, wireless communication networks have played a vital role in environmental monitoring and other data-driven applications. Even though these networks often struggle with limited energy and redundant data transmissions. Moreover, traditional routing protocols, such as the Cross-layer Opportunistic Routing Protocol (CORP), rely heavily on static routing decisions with fixed-cost functions, leading to a lack of adaptability. To address these issues, this study proposes a Mistral 7B-based Cross-layer Optimization (M7BCO), which integrates adaptive reasoning and prompt-based telemetry compression for energy-aware decisions. The proposed M7BCO model utilizes a Partially Informed Sparse Autoencoder (PISA) to select a minimal subset of informative nodes by learning spatial correlations while preserving data reconstructability. Then, the proposed M7BCO model generates a real-time decision for next-hop selection and transmits power adjustment as it replaces the static optimization with adaptive reasoning. Unlike pure sequential models, the proposed model introduced a lightweight training loop between PISA telemetry selection and Mistral 7B adaptive reasoning. From the results, the proposed M7BCO model achieved better results when compared to the existing CORP model in terms of Energy Efficiency (EE) of 22.5, 65.3, and 100.2 mJ for 150, 300, and 500 nodes respectively.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272565","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

Secure Data Routing and Authentication Framework With Privacy Preservation for Multimedia Sensor Environments 多媒体传感器环境中具有隐私保护的安全数据路由和认证框架

IF 0.5

Internet Technology Letters Pub Date : 2025-10-08 DOI: 10.1002/itl2.70160

Rami Baazeem

{"title":"Secure Data Routing and Authentication Framework With Privacy Preservation for Multimedia Sensor Environments","authors":"Rami Baazeem","doi":"10.1002/itl2.70160","DOIUrl":"https://doi.org/10.1002/itl2.70160","url":null,"abstract":"<div>\u0000 \u0000 <p>Multimedia Wireless Sensor Networks (MWSNs) form a crucial backbone of smart cities, enabling real-time data collection and communication across diverse applications. However, ensuring secure routing, authentication, and efficient data transmission remains a major challenge due to limited resources and susceptibility to malicious attacks. This study proposes a trust-based routing and authentication framework that integrates lightweight cryptographic mechanisms with a three-phase certification process involving key generation, encryption, and decryption. A Trust Authority (TA) manages node registration, certificate issuance, and revocation, while cluster head (CH) selection is optimized using energy and distance-based formulations to balance load and reduce energy consumption. The model employs both identification-based and control-packet authentication, ensuring confidentiality, non-repudiation, and protection against internal attacks. Experimental evaluation on MATLAB with 100–500 sensor nodes and varying malicious ratios demonstrates that the proposed framework achieves higher detection accuracy, lower certification delay, reduced energy consumption, and improved throughput compared to existing approaches, making it suitable for secure smart city deployments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271985","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

Construction of IIoT-Based Smart Education Environment and Innovation of Practical Teaching Mode for Teacher Training Students 构建基于iiot的智慧教育环境创新师范生实践教学模式

IF 0.5

Internet Technology Letters Pub Date : 2025-10-07 DOI: 10.1002/itl2.70163

Juan Yu, Rong Xi

引用次数: 0

Multi-Agent Based Distributed Computing for Photovoltaic Systems Economic Dispatch Using Modified Exact Diffusion Strategy 基于修正精确扩散策略的光伏系统经济调度多智能体分布式计算

IF 0.5

Internet Technology Letters Pub Date : 2025-10-07 DOI: 10.1002/itl2.70124

Wenjie Zhu

{"title":"Multi-Agent Based Distributed Computing for Photovoltaic Systems Economic Dispatch Using Modified Exact Diffusion Strategy","authors":"Wenjie Zhu","doi":"10.1002/itl2.70124","DOIUrl":"https://doi.org/10.1002/itl2.70124","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid deployment of photovoltaic (PV) systems and the transition toward decentralized energy infrastructures, traditional centralized economic dispatch methods are increasingly challenged by scalability bottlenecks, communication overhead, and vulnerability to single-point failures. These issues are further exacerbated by the dynamic and distributed nature of PV-based microgrids, where plug-and-play devices, intermittent generation, and privacy constraints demand localized decision-making and coordination. To address these challenges, this paper proposes a fully distributed economic dispatch framework based on a multi-agent system and a Modified Exact Diffusion Algorithm (MEDA). The framework models PV units, battery storage, flexible loads, and grid interfaces as autonomous agents that interact through peer-to-peer communication, collaboratively achieving global optimality without centralized supervision. A SOC-aware battery cost model, dynamic electricity pricing, and quadratic line loss modeling are integrated to enhance practical realism. Simulation results on a modified IEEE 33-bus microgrid show that the proposed approach significantly outperforms centralized and existing distributed methods in terms of cost reduction, convergence speed, resilience to communication failures, and adaptability to agent dynamics.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271941","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

Motion Heart Rate Anomaly Detection Based on Variational Autoencoder in Multiple Wearable Device Scenarios 多可穿戴设备场景下基于变分自编码器的运动心率异常检测

IF 0.5

Internet Technology Letters Pub Date : 2025-10-07 DOI: 10.1002/itl2.70133

Yang Yu

{"title":"Motion Heart Rate Anomaly Detection Based on Variational Autoencoder in Multiple Wearable Device Scenarios","authors":"Yang Yu","doi":"10.1002/itl2.70133","DOIUrl":"https://doi.org/10.1002/itl2.70133","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep learning and wearable devices for heart rate detection have been widely applied in sports for real-time body monitoring. However, existing deep networks such as convolutional networks (CNNs) and recurrent neural networks (RNNs) are unable to model the spatiotemporal features of time series signals. Moreover, these models are unable to model the uncertainty in complex motion scenes. To this end, this article constructs an effective abnormal heart rate detection system based on a variant variational autoencoder. First, the photoplethysmography (PPG) signals from different user terminals are collected and transmitted to the server through the wireless sensor network. Then, on the server side, we deployed a novel variant variational autoencoder (VAE) by exploiting the 1D convolution operation and the temporal convolutional network (TCN) module for spatiotemporal feature extraction of time series. Moreover, the VAE can effectively alleviate uncertainty in motion scenes. Finally, we conducted comparative experiments on our self-built dataset of abnormal heart rate during exercise, and the experimental results showed that the proposed model achieves the highest anomaly detection performance.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271944","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

Federated Edge Intelligence: A Collaborative Learning Framework for Multi-Object Detection on Mobile Platforms 联邦边缘智能：移动平台上多目标检测的协作学习框架

IF 0.5

Internet Technology Letters Pub Date : 2025-10-07 DOI: 10.1002/itl2.70145

Miao Yan

引用次数: 0

User Intent Understanding and Service Classification in English Tutoring Systems via Large Language Models Over Wireless Communication Networks 基于无线通信网络的大型语言模型英语辅导系统的用户意图理解与服务分类

IF 0.5

Internet Technology Letters Pub Date : 2025-10-06 DOI: 10.1002/itl2.70154

Hua Lian

引用次数: 0

Large Model-Enhanced CNN–Transformer Architecture for Adaptive Music Quality Classification in Wireless Communication Networks 无线通信网络中自适应音乐质量分类的大模型增强CNN-Transformer架构

IF 0.5

Internet Technology Letters Pub Date : 2025-10-04 DOI: 10.1002/itl2.70156

Tianyu Chen

{"title":"Large Model-Enhanced CNN–Transformer Architecture for Adaptive Music Quality Classification in Wireless Communication Networks","authors":"Tianyu Chen","doi":"10.1002/itl2.70156","DOIUrl":"https://doi.org/10.1002/itl2.70156","url":null,"abstract":"<div>\u0000 \u0000 <p>This letter presents an enhanced convolutional neural network (CNN)–transformer architecture integrated with large model (LM) capabilities for adaptive music quality classification in wireless communication networks (WCNs). The proposed approach combines the global feature learning strength of transformer encoders with the local pattern recognition abilities of CNNs while leveraging LM knowledge for improved audio signal understanding. To enhance classification accuracy, we first preprocess the music signal data through channel-aware normalization and feature standardization. Subsequently, we employ a multi-head attention mechanism from transformer networks to capture long-range dependencies in music features affected by wireless transmission while utilizing CNN layers to extract localized audio patterns. Finally, we incorporate inception modules to achieve multi-scale feature fusion and complete the music quality classification task. Experimental validation on the MusicCaps dataset demonstrates that our model achieves 97.8% classification accuracy, with precision, recall, and F1-score all exceeding 97.5%, outperforming existing approaches for music quality assessment in wireless environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223817","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

Intelligent English Teaching Video Traffic Classification in Wireless Communication Networks via Large Model-Enhanced Sparse Attention Vision Transformer 基于大模型增强稀疏注意视觉变压器的无线通信网络智能英语教学视频流量分类

IF 0.5

Internet Technology Letters Pub Date : 2025-10-04 DOI: 10.1002/itl2.70153

Jinjin Liu

引用次数: 0

Large Model Framework for Wireless Network Optimization in Smart Physical Education Environments 智能体育环境下无线网络优化的大型模型框架

IF 0.5

Internet Technology Letters Pub Date : 2025-10-02 DOI: 10.1002/itl2.70151

BingYang Liu, Yang Liu

{"title":"Large Model Framework for Wireless Network Optimization in Smart Physical Education Environments","authors":"BingYang Liu, Yang Liu","doi":"10.1002/itl2.70151","DOIUrl":"https://doi.org/10.1002/itl2.70151","url":null,"abstract":"<div>\u0000 \u0000 <p>Modern physical education increasingly relies on wireless communication networks to deliver immersive training experiences through wearable devices, motion-tracking sensors, and real-time performance analytics. However, optimizing wireless network performance in dynamic physical education environments presents complex challenges due to rapidly changing user mobility patterns, varying signal interference from athletic equipment, and fluctuating bandwidth demands during different exercise activities. This letter proposes a novel wavelet-enhanced large model framework that integrates wavelet transform signal processing with enhanced position encoding in transformer architectures to predict and optimize wireless network performance for physical education applications. Experimental validation demonstrates that our proposed model accurately captures non-stationary behavior and abrupt changes in wireless network performance during various physical activities. The RMSE and MAPE metrics show improvements of 29.9% and 2.9%, respectively, compared to baseline transformer models, and 34.5% and 3.4% improvements compared to LSTM approaches, providing a novel technical solution for smart physical education network management.</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":"145223846","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