Internet Technology Letters最新文献

{"title":"A Smoothing Technique for Objective Penalty Functions in Inequality-Constrained Optimization: Applications in Wireless Sensor Networks and 5G Communication","authors":"Darpan Sood,&nbsp;Amanpreet Singh,&nbsp;Mohammed I. Habelalmateen,&nbsp;Malika Anwar Siddiqui,&nbsp;Shaveta Kaushal,&nbsp;Sudan Jha,&nbsp;Deepak Prashar,&nbsp;Rachit Garg","doi":"10.1002/itl2.70095","DOIUrl":"https://doi.org/10.1002/itl2.70095","url":null,"abstract":"<div>\u0000 \u0000 <p>This manuscript provides a smoothing technique for objective penalty functions in inequality-constrained optimization problems. A non-smooth penalty function is defined which is subjected to a new smoothing technique to make it smooth. The error estimates for the original and the smoothed problem are discussed. A procedure is illustrated for the development of the solution of the inequality-constrained optimization problem and is shown to be convergent under certain specified conditions. The same can be incorporated in various application areas like Wireless Sensor Networks in the form of giving penalties to sensor nodes not fulfilling the network performance criteria and also in some other aspects like 5G communication.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038183","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":"Resource Scheduling Model in Computing Power Network: An Efficient and Low-Carbon Approach Using Game Theory","authors":"Yunhao Zhang,&nbsp;Zhiyu Jiang,&nbsp;Xiuping Guo,&nbsp;Xueying Zhai,&nbsp;Yunfeng Peng","doi":"10.1002/itl2.70111","DOIUrl":"https://doi.org/10.1002/itl2.70111","url":null,"abstract":"<div>\u0000 \u0000 <p>Data centers are facing high energy consumption and high carbon emissions. China set up a computing power network, to transmit data from the east to clean electricity-rich western data centers for processing. This letter proposes a resource scheduling model using game theory to sort out the conflicting demands within the network. The interrelationships and constraints among data processing task requirements, computing power resources, clean electricity, and communication network resources were formulated. The resource scheduling scheme was developed through conflict analysis of the two competing entities. The results show the strategy supports the operation of the computing power network, which decreases the utilization of thermal electricity and enhances the quality of task completion.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038181","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":"Safety Fault Prediction and Diagnosis of Power Measurement Equipment Based on 6G Big Data Analysis","authors":"Yin Gao","doi":"10.1002/itl2.70107","DOIUrl":"https://doi.org/10.1002/itl2.70107","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of 6G networks has revolutionized power system monitoring by enabling ultra-fast, low-latency communication, which is essential for real-time fault prediction and diagnosis in power measurement equipment. However, conventional fault diagnostic methods often rely on centralized data processing, which raises significant concerns about data privacy threats, latency, and inefficiencies in real-time problem identification. We provide a Big Data-Driven Predictive Analytics with Federated Learning (BD-PA-FL) platform to address these issues. Without sending sensitive raw data, this novel method enables decentralized, privacy-preserving model training across numerous edge devices. By utilizing distributed big data and safeguarding data privacy, BD-PA-FL enables decentralized predictive analytics through FL. It avoids centralized data pooling, which lowers latency and improves real-time, privacy-aware fault detection in contrast to traditional fault diagnosis. To enable effective and intelligent fault prediction at the network edge, the proposed framework incorporates several essential elements. First, vital operating metrics from power equipment are captured by real-time sensor data collection. After that, insightful feature extraction methods are employed to identify significant patterns in the unprocessed data, enabling the detection of anomalies at an early stage. FL algorithms allow the system to collaboratively train predictive models across distributed nodes without sharing sensitive data, preserving privacy. By leveraging a cloud-edge AI architecture, the system ensures scalability, low latency, and effective resource utilization for predictive maintenance. Experimental results confirm that the BD-PA-FL framework significantly improves fault detection accuracy, reduces downtime, and enhances overall grid reliability in a secure, 6G-enabled environment.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012330","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":"LLM-Enhanced PSO for ECU Configuration in Wireless-Supported Distribution Network Self-Restoration","authors":"Hongtao Mao,&nbsp;Yifeng Wang,&nbsp;Bin Dong,&nbsp;Yangyang Miao,&nbsp;Wu Ma,&nbsp;Jun Wang","doi":"10.1002/itl2.70135","DOIUrl":"https://doi.org/10.1002/itl2.70135","url":null,"abstract":"<div>\u0000 \u0000 <p>Rapid fault self-restoration in complex power distribution networks is crucial. Edge Computing Units (ECUs) offer decentralized control, but their optimal configuration is challenging. This paper proposes a Large Language Model (LLM) enhanced Particle Swarm Optimization (PSO) framework for ECU configuration, explicitly considering advanced wireless communication (e.g., 5G/6G, LoRa) characteristics. The LLM aids in intelligent population initialization and adaptive particle guidance within PSO. This approach aims to optimize ECU placement and inter-ECU data exchange. Simulations on IEEE test systems show that the LLM-enhanced PSO significantly improves ECU configurations, reduces communication delays, and enhances self-restoration performance, thereby bolstering smart grid resilience.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012458","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 Multi-User Collaborative Recommendation Mechanism for Career Planning in Online Learning Over Edge Networks","authors":"Zhen Zhang,&nbsp;Guixin Luo,&nbsp;Jieyu Zhang","doi":"10.1002/itl2.70126","DOIUrl":"https://doi.org/10.1002/itl2.70126","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, online learning platforms have gained popularity, particularly in the realm of career planning and skill development. However, most existing recommendation systems fail to fully integrate multi-behavioral user data and collaborative group preferences. This paper presents a Multi-User Collaborative Recommendation Mechanism for Career Planning in Online Learning (MCR-MCL), which combines multi-behavioral interaction data, group consensus modeling, and edge Networks to enhance personalized career planning recommendations. By leveraging edge network deployment, our system enables low-latency, localized updates that dynamically adapt to users' behaviors without frequent reliance on centralized cloud servers. We propose an innovative approach that leverages Graph Convolutional Networks (GCNs) to process user-item interactions and a behavioral independence modeling mechanism to avoid over-reliance on a single interaction type. We evaluate the effectiveness of the proposed mechanism using two real-world datasets—CareerMOOC and CareerEdNet—and demonstrate that our model significantly outperforms existing state-of-the-art methods in terms of recommendation accuracy, diversity, and low-latency adaptability through edge-based processing. The experimental results indicate that MCR-MCL can provide highly relevant, diverse, and dynamic recommendations that are essential for career planning in the context of online learning.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998944","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":"Effective Centralized Power Control and Management of Nano-Grid Using DT Based Novel Distributed Framework","authors":"Jarabala Ranga,&nbsp;Gopinath Palai,&nbsp;Rabi N. Satpathy","doi":"10.1002/itl2.70080","DOIUrl":"https://doi.org/10.1002/itl2.70080","url":null,"abstract":"<div>\u0000 \u0000 <p>Nano-grid is an independent hybrid sustainable framework which uses both renewable and non-renewable power sources to continuously supply energy to load. Nano-grid finds its possibilities for the integration of distributed energy sources for realizing versatile and efficient energy management systems for future homes, local communities, and buildings. Nano-grid's energy trading system effectiveness might depend on various factors including core efficient management components such as energy storage systems (ESS) and renewable energy devices. Smart advanced functions in consumer devices and their unpredictable usage patterns result in unpredictable fluctuations in consumption of power. These fluctuations pose significant challenges in stability and quality of the power rid and create complex power imbalance issues which become harder to control and manage. Innovative power control and management models are essential to solve these issues in the nano-grid. In recent times, machine learning algorithms can be used to predict, track the current conditions, and make appropriate adjustments to the quality settings of power. In this research, effective centralized power control and management of the nano-grid using DT-based novel distributed framework is presented. This system utilizes a novel distributed framework based on a decision tree to enhance the agility and stability of complex and large-scale power systems. Performance measures like accuracy, F1-score, and RMSE are used to evaluate the performance of this system. This system will achieve better centralized power control and management.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998946","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":"EfficientDet-EdgeUAV: A Multi-Scale Fusion Architecture for Target Detection in UAV Imagery With Edge Computing Optimization","authors":"Chang Su,&nbsp;Xin Deng,&nbsp;Dehan Xue","doi":"10.1002/itl2.70118","DOIUrl":"https://doi.org/10.1002/itl2.70118","url":null,"abstract":"<div>\u0000 \u0000 <p>To overcome cloud computing's limitations—high latency and costly data transfers that hinder rapid UAV detection—plus the challenges of spotting tiny targets against complex backgrounds in wide-field aerial views, an edge computing solution with its specialized lightweight network: EfficientDet-EdgeUAV is proposed. The network employs a structurally optimized EfficientNet backbone through lightweight architecture modifications, integrating a Squeeze-and-Excitation attention mechanism to mitigate interference from complex backgrounds in target detection. The network architecture enhances small object detection by incorporating large-scale feature layers into the pyramid structure of the neck and applying lightweight architectural optimization to the neck module. The architecture further enhances detection robustness by implementing multi-scale feature fusion in the neck module, which strategically combines shallow-layer spatial details and deep-layer semantic representations to improve discernment of small objects with blurred boundaries. Through extensive experiments that comprehensively evaluate and validate the effectiveness of the proposed method, the experimental results demonstrate superior detection accuracy and efficiency on the VisDrone dataset compared to baseline and state-of-the-art methods. This demonstrates that the proposed method achieves exceptional effectiveness in real-time UAV imaging scenarios, providing critical technical references for civilian applications of drone technology in power line inspection, geological exploration, and search and rescue operations.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935027","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":"Enhancing Car Tracking Systems With DNN-LoRa","authors":"Malak Abid Ali Khan,&nbsp;Senlin Luo","doi":"10.1002/itl2.70130","DOIUrl":"https://doi.org/10.1002/itl2.70130","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper outlines a fusion of deep neural network and LoRa technology for car tracking optimization. LoRa's SX1301 gateway (GW) applies the Bayesian game parameter selection (BGPS) approach for switching the transmission power at the network server. At the same time, the car node (CN) uses a hybrid model to change the spreading factor and data rate. By reducing power losses among GWs, BGPS substantially increases the packet success rate (PSR) at the CN. The hybrid model enables adaptive decision-making, resulting in improved tracking precision and reduced latency with efficient energy usage. However, it exhibits a 95.9% PSR with increased latency noted at the lower bandwidth.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935026","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":"Integrating Big Data and AI for Network Security in 6G to Enhance University Financial Management","authors":"Jun Liang,&nbsp;Ling Pu,&nbsp; WeiweiSun","doi":"10.1002/itl2.70106","DOIUrl":"https://doi.org/10.1002/itl2.70106","url":null,"abstract":"<p>In the 6G network structures, the integration of Big Data (BD) and artificial intelligence (AI) is beneficial for the purpose of improving cybersecurity in university financial management systems. So, the integration of the BD and AI in the 6G structures are suggested in this study. Then, the conventional centralized security systems are ineffective in the rapid digitalization of financial transactions. Because these conventional systems are susceptible to single points of failure (SPF), delayed threat detection, and data privacy breaches. In the real-time (RT) financial backgrounds, these conventional systems face difficulties in protecting the network against advanced cyber threats. These situations will call for a decentralized, adaptive, and privacy-preserving (PP) security framework in the rapidly evolving 6G structures. This demanded framework may help in anomaly detection (AD) in financial transactions without affecting vital data. Thus, a novel federated learning (FL)-based AD in financial security (FL-AD-FS) framework is suggested in this study. To train the AI models collaboratively over several edge devices, this suggested model utilizes FL. This application will also ensure the privacy of the data. Then, in financial operations, the RT AD and threat mitigation was facilitated by the system, as it integrates with 6G-enabled (EC) edge computing. The simulations were conducted; from the outcomes, it is clear that the suggested FL-AD-FS model executes better by reducing false positive rates (FPRs), increasing detection (ACC) accuracy, and minimizing latency. In university backgrounds, secure, fast, and reliable monitoring of financial transactions was facilitated by this suggested method. For revolutionizing cybersecurity in digital financial systems, the potential of the integration of the FL, AI, and 6G technologies is demonstrated by the FL-AD-FS framework. For modern university financial management, this suggested method creates a customized scalable, secure, and privacy-aware solution.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.70106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934929","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}

{"title":"MAGAN-RT: A Lightweight Adversarial Style Transfer Network for Real-Time Cartoonization on Low-Power Edge Devices","authors":"Peng Guo","doi":"10.1002/itl2.70104","DOIUrl":"https://doi.org/10.1002/itl2.70104","url":null,"abstract":"<div>\u0000 \u0000 <p>Recent advances in neural style transfer (NST) and generative adversarial networks (GANs) have enabled photorealistic and artistic image stylization. However, deploying such models on resource-constrained edge devices remains challenging due to their high computational and memory demands. In this paper, we propose MAGAN-RT, a lightweight adversarial style transfer framework optimized for real-time cartoon-style transformation on low-power mobile and embedded platforms. MAGAN-RT integrates depthwise separable convolutions, inverted bottleneck residual blocks, and a multi-scale perceptual distillation strategy with auxiliary RGB supervision to enable efficient and expressive stylization. Furthermore, a real-image-based adversarial loss is employed to enhance realism while avoiding the artifacts commonly inherited from teacher models. Experimental results demonstrate that MAGAN-RT outperforms existing lightweight and mobile-compatible style transfer networks in both visual quality and runtime efficiency. It achieves state-of-the-art LPIPS, FID, and SSIM scores, while maintaining sub-10 ms inference latency on commercial smartphones, making it suitable for real-time applications such as mobile AR and video filters.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927598","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}