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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,&nbsp;Mutasem Azmi Alkarablieh,&nbsp;Odai Alabadleh,&nbsp;Malek Suliman Alshnaikat,&nbsp;Mahmoud Abushawali,&nbsp;Monther S. Al-atoum,&nbsp;Musab Alqudah,&nbsp;Anas Atef Shamaileh,&nbsp;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
IF 0.9
Internet Technology Letters Pub Date : 2025-06-29 DOI: 10.1002/itl2.70066
Tao Feng, Huayu Zhang, Yi Ouyang
{"title":"Context-Aware Satellite Remote Sensing Fire Point Detection Based on Energy Scores","authors":"Tao Feng,&nbsp;Huayu Zhang,&nbsp;Yi Ouyang","doi":"10.1002/itl2.70066","DOIUrl":"https://doi.org/10.1002/itl2.70066","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile deployable deep models are crucial for forest fire point detection based on satellite remote sensing images. Existing convolutional neural networks (CNNs) are limited by their context-aware capabilities and the Transformer requires quadratic computational complexity for modeling long-distance dependency relationships, making it difficult to effectively deploy the model on mobile devices. To this end, this article constructs a context-aware Mamba network based on energy-based distillation for satellite remote sensing fire point detection. Firstly, we construct a feature extraction backbone network based on the Mamba module, which can achieve long-distance dependence modeling with linear computational complexity. In addition, we introduce a distillation learning mechanism based on energy score to improve the forest fire recognition performance. The results of the publicly available satellite remote sensing fire dataset have confirmed that our proposed method achieves the highest F1-Score in fire detection tasks.</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":"144514971","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
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
IF 0.9
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,&nbsp;Prerna Mann,&nbsp;Navdeep Bohra,&nbsp;Kalpna Sagar,&nbsp;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
IF 0.9
Internet Technology Letters Pub Date : 2025-06-29 DOI: 10.1002/itl2.70067
Saumya Chaturvedi, Vivek Ashok Bohara
{"title":"Future Directions in Multiple Access for 6G: Emerging Paradigms and Insights","authors":"Saumya Chaturvedi,&nbsp;Vivek Ashok Bohara","doi":"10.1002/itl2.70067","DOIUrl":"https://doi.org/10.1002/itl2.70067","url":null,"abstract":"<div>\u0000 \u0000 <p>This work presents a comprehensive performance assessment of various multiple access (MA) schemes for downlink communication, with a focus on rate-splitting multiple access (RSMA), sparse code multiple access (SCMA), and power-domain non-orthogonal multiple access (PD-NOMA), in comparison to the traditional orthogonal multiple access (OMA) scheme. The study analyzes these schemes under imperfect decoding conditions, assessing their sum-rate, fairness, and outage performance. Simulation results show that SCMA achieves better sum-rate and outage performance than other MA schemes, even under conditions of imperfect decoding. Furthermore, RSMA provides better fairness at lower power levels, while SCMA achieves superior fairness at higher power levels. By integrating a thorough survey with simulation-based insights, this work underscores the distinct advantages of SCMA and RSMA, positioning them as promising candidates for future 6G wireless communication systems.</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":"144514592","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 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
Revolutionizing Cyber Defense: Leveraging Generative AI for Adaptive Threat Hunting 革命性的网络防御：利用生成式人工智能进行自适应威胁搜索
IF 0.9
Internet Technology Letters Pub Date : 2025-06-26 DOI: 10.1002/itl2.70039
Aditya K. Sood, Sherali Zeadally
{"title":"Revolutionizing Cyber Defense: Leveraging Generative AI for Adaptive Threat Hunting","authors":"Aditya K. Sood,&nbsp;Sherali Zeadally","doi":"10.1002/itl2.70039","DOIUrl":"https://doi.org/10.1002/itl2.70039","url":null,"abstract":"<p>Adaptive threat hunting, powered by Generative AI (GenAI), is reshaping the cybersecurity landscape, equipping analysts with the tools to predict, detect, and dynamically mitigate cyber threats. Adaptive threat hunting is critical because it enables an organization to detect threats proactively in the infrastructure, thereby reducing the risks and impacts. We present an adaptive threat hunting enhancement model using GenAI, design considerations, and coverage of real-world use cases, including future considerations.</p>","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":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492677","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
Ensemble Classification-Based Spectrum Sensing Using Support Vector Machine for Cognitive Radio Networks 认知无线电网络中基于集成分类的支持向量机频谱感知
IF 0.9
Internet Technology Letters Pub Date : 2025-06-25 DOI: 10.1002/itl2.70063
Manpreet Kaur, Raj Singh, Sandeep Kumar
{"title":"Ensemble Classification-Based Spectrum Sensing Using Support Vector Machine for Cognitive Radio Networks","authors":"Manpreet Kaur,&nbsp;Raj Singh,&nbsp;Sandeep Kumar","doi":"10.1002/itl2.70063","DOIUrl":"https://doi.org/10.1002/itl2.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>As next-generation communication systems require more spectrum-intensive applications, the challenge of spectrum scarcity becomes increasingly significant. A promising solution is cognitive radio networks (CRNs), which optimize the use of spectrum, a valuable and sharable natural resource that should not be wasted. To design efficient and sustainable networks for the future, it is crucial to ensure that spectrum sensing is not only accurate and rapid, but also energy-efficient. Spectrum sensing is a critical aspect of CRNs, and this study is mainly focused on it. This research employs a supervised Support Vector Machines (SVM) algorithm to detect primary users (PU). We analyze linear, polynomial, and Gaussian RBF SVM variants and enhance performance using an ensemble classification approach. Simulations show the ensemble classifier achieves the best results.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482044","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
TinyFed: Lightweight Federated Learning for Constrained IoT Devices TinyFed：用于受限物联网设备的轻量级联邦学习
IF 0.9
Internet Technology Letters Pub Date : 2025-06-22 DOI: 10.1002/itl2.70061
Claudio Jr. N. da Silva, Maycon L. M. Peixoto, Gustavo B. Figueiredo, Cassio V. S. Prazeres
{"title":"TinyFed: Lightweight Federated Learning for Constrained IoT Devices","authors":"Claudio Jr. N. da Silva,&nbsp;Maycon L. M. Peixoto,&nbsp;Gustavo B. Figueiredo,&nbsp;Cassio V. S. Prazeres","doi":"10.1002/itl2.70061","DOIUrl":"https://doi.org/10.1002/itl2.70061","url":null,"abstract":"<p>TinyML enables machine learning inference on microcontrollers with limited resources. Extending this to a collaborative setting led to Tiny Federated Learning (TinyFL). This article presents TinyFed, a lightweight framework that supports the full federated learning cycle—from local training to model aggregation and redistribution. TinyFed was validated on ESP32 devices using a neural network with four inputs, three hidden layers, and two outputs to detect temperature, humidity, luminosity, and voltage anomalies. Local training achieved accuracies of up to 99.47%.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339525","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
Zero Trust Networks and Federated Unlearning Based 6G Edge Networks: Attack Scenario, Security Model and Future Directions 零信任网络和基于联邦学习的6G边缘网络：攻击场景、安全模型和未来方向
IF 0.9
Internet Technology Letters Pub Date : 2025-06-22 DOI: 10.1002/itl2.70056
Nishat Mahdiya Khan, Pronaya Bhattacharya, Haipeng Liu, Zhu Zhu, Thippa Reddy Gadekallu
{"title":"Zero Trust Networks and Federated Unlearning Based 6G Edge Networks: Attack Scenario, Security Model and Future Directions","authors":"Nishat Mahdiya Khan,&nbsp;Pronaya Bhattacharya,&nbsp;Haipeng Liu,&nbsp;Zhu Zhu,&nbsp;Thippa Reddy Gadekallu","doi":"10.1002/itl2.70056","DOIUrl":"https://doi.org/10.1002/itl2.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>The dynamic interplay between federated learning (FL) and federated unlearning (FU) introduces vulnerabilities, particularly the slow poisoning attack scenario by malicious adversaries. The attack proceeds where adversaries can gradually degrade global model performance over successive update cycles. In this letter, we propose a blueprint architecture that integrates zero trust networks (ZTNs) into both the unlearning (FU) request and the client admission (FL) stages to counteract these threats. By enforcing continuous client verification and rigorous risk assessment, our vision ensures that only authenticated and reliable updates contribute to the global model, thereby preserving model integrity and safeguarding sensitive data. Promising future research directions and open challenges are also discussed.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339524","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
Online Auxiliary Evaluation of Physical Education Teaching Based on Facial Expression Recognition 基于面部表情识别的体育教学在线辅助评价
IF 0.9
Internet Technology Letters Pub Date : 2025-06-19 DOI: 10.1002/itl2.70065
Yuan Gao
{"title":"Online Auxiliary Evaluation of Physical Education Teaching Based on Facial Expression Recognition","authors":"Yuan Gao","doi":"10.1002/itl2.70065","DOIUrl":"https://doi.org/10.1002/itl2.70065","url":null,"abstract":"<div>\u0000 \u0000 <p>Internet plus technology and artificial intelligence technology are widely used in online sports teaching and curriculum evaluation tasks. However, existing deep network-based online facial expression recognition is susceptible to complex scenarios such as lighting, and occlusion, which directly affect the accuracy of course evaluation. To this end, this paper designs an emotion recognition network based on spatiotemporal hypergraph convolution for robust online emotion analysis. Specifically, we collect facial video sequences from different clients and generate corresponding facial landmark sequences. On the server side, an effective spatiotemporal hypergraph convolutional network is deployed, in which the hypergraph convolution module can exploit high-order relationships between facial landmarks. To verify the effectiveness of our model, we conducted extensive comparative experiments on two public expression datasets and our self-built dataset. The experimental results show that the proposed model obtains higher accuracy and effectively improves the quality of physical education teaching evaluation.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323733","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
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