IoT最新文献 - Book学术

An IoT-Enabled Digital Twin Architecture with Feature-Optimized Transformer-Based Triage Classifier on a Cloud Platform 基于云平台上基于变压器的特征优化分类器的支持物联网的数字孪生架构

IoT Pub Date : 2025-11-26 DOI: 10.3390/iot6040073

Haider Q. Mutashar, Hiba A. Abu-Alsaad, Sawsan M. Mahmoud

{"title":"An IoT-Enabled Digital Twin Architecture with Feature-Optimized Transformer-Based Triage Classifier on a Cloud Platform","authors":"Haider Q. Mutashar, Hiba A. Abu-Alsaad, Sawsan M. Mahmoud","doi":"10.3390/iot6040073","DOIUrl":"https://doi.org/10.3390/iot6040073","url":null,"abstract":"It is essential to assign the correct triage level to patients as soon as they arrive in the emergency department in order to save lives, especially during peak demand. However, many healthcare systems estimate the triage levels by manual eyes-on evaluation, which can be inconsistent and time consuming. This study creates a full Digital Twin-based architecture for patient monitoring and automated triage level recommendation using IoT sensors, AI, and cloud-based services. The system can monitor all patients’ vital signs through embedded sensors. The readings are used to update the Digital Twin instances that represent the present condition of the patients. This data is then used for triage prediction using a pretrained model that can predict the patients’ triage levels. The training of the model utilized the synthetic minority over-sampling technique, combined with Tomek links to lessen the degree of data imbalance. Additionally, Lagrange element optimization was applied to select those features of the most informative nature. The final triage level is predicted using the Tabular Prior-Data Fitted Network, a transformer-based model tailored for tabular data classification. This combination achieved an overall accuracy of 87.27%. The proposed system demonstrates the potential of integrating digital twins and AI to improve decision support in emergency healthcare environments.","PeriodicalId":511512,"journal":{"name":"IoT","volume":"6 4","pages":"73-73"},"PeriodicalIF":0.0,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2624-831X/6/4/73/pdf?version=1764171176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147888236","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: 1

Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach 推进 5G 物联网中的 XSS 检测：前沿人工神经网络方法

IoT Pub Date : 2024-07-25 DOI: 10.3390/iot5030022

Rabee Alqura’n, Mahmoud AlJamal, Issa AL-Aiash, A. Alsarhan, Bashar Khassawneh, Mohammad Aljaidi, Rakan Alanazi

{"title":"Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach","authors":"Rabee Alqura’n, Mahmoud AlJamal, Issa AL-Aiash, A. Alsarhan, Bashar Khassawneh, Mohammad Aljaidi, Rakan Alanazi","doi":"10.3390/iot5030022","DOIUrl":"https://doi.org/10.3390/iot5030022","url":null,"abstract":"The rapid expansion of the Internet of Things (IoT) and the advancement of 5G technology require strong cybersecurity measures within IoT frameworks. Traditional security methods are insufficient due to the wide variety and large number of IoT devices and their limited computational capabilities. With 5G enabling faster data transmission, security risks have increased, making effective protective measures essential. Cross-Site Scripting (XSS) attacks present a significant threat to IoT security. In response, we have developed a new approach using Artificial Neural Networks (ANNs) to identify and prevent XSS breaches in IoT systems over 5G networks. We significantly improved our model’s predictive performance by using filter and wrapper feature selection methods. We validated our approach using two datasets, NF-ToN-IoT-v2 and Edge-IIoTset, ensuring its strength and adaptability across different IoT environments. For the NF-ToN-IoT-v2 dataset with filter feature selection, our Bilayered Neural Network (2 × 10) achieved the highest accuracy of 99.84%. For the Edge-IIoTset dataset with filtered feature selection, the Trilayered Neural Network (3 × 10) achieved the best accuracy of 99.79%. We used ANOVA tests to address the sensitivity of neural network performance to initial conditions, confirming statistically significant improvements in detection accuracy. The ANOVA results validated the enhancements across different feature selection methods, demonstrating the consistency and reliability of our approach. Our method demonstrates outstanding accuracy and robustness, highlighting its potential as a reliable solution for enhancing IoT security in the era of 5G networks.","PeriodicalId":511512,"journal":{"name":"IoT","volume":"57 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805923","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

Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0 多医院管理：生命体征物联网数据与弹性技术相结合，支持医疗保健 4.0

IoT Pub Date : 2024-06-08 DOI: 10.3390/iot5020019

Gabriel Souto Fischer, G. D. O. Ramos, Cristiano André da Costa, A. Alberti, Dalvan Griebler, Dhananjay Singh, R. Righi

{"title":"Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0","authors":"Gabriel Souto Fischer, G. D. O. Ramos, Cristiano André da Costa, A. Alberti, Dalvan Griebler, Dhananjay Singh, R. Righi","doi":"10.3390/iot5020019","DOIUrl":"https://doi.org/10.3390/iot5020019","url":null,"abstract":"Smart cities can improve the quality of life of citizens by optimizing the utilization of resources. In an IoT-connected environment, people’s health can be constantly monitored, which can help identify medical problems before they become serious. However, overcrowded hospitals can lead to long waiting times for patients to receive treatment. The literature presents alternatives to address this problem by adjusting care capacity to demand. However, there is still a need for a solution that can adjust human resources in multiple healthcare settings, which is the reality of cities. This work introduces HealCity, a smart-city-focused model that can monitor patients’ use of healthcare settings and adapt the allocation of health professionals to meet their needs. HealCity uses vital signs (IoT) data in prediction techniques to anticipate when the demand for a given environment will exceed its capacity and suggests actions to allocate health professionals accordingly. Additionally, we introduce the concept of multilevel proactive human resources elasticity in smart cities, thus managing human resources at different levels of a smart city. An algorithm is also devised to automatically manage and identify the appropriate hospital for a possible future patient. Furthermore, some IoT deployment considerations are presented based on a hardware implementation for the proposed model. HealCity was evaluated with four hospital settings and obtained promising results: Compared to hospitals with rigid professional allocations, it reduced waiting time for care by up to 87.62%.","PeriodicalId":511512,"journal":{"name":"IoT","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369744","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

Investigating Radio Frequency Vulnerabilities in the Internet of Things (IoT) 调查物联网 (IoT) 中的射频漏洞

IoT Pub Date : 2024-06-06 DOI: 10.3390/iot5020018

Eirini Anthi, Lowri Williams, Vasilis Ieropoulos, Theodoros Spyridopoulos

{"title":"Investigating Radio Frequency Vulnerabilities in the Internet of Things (IoT)","authors":"Eirini Anthi, Lowri Williams, Vasilis Ieropoulos, Theodoros Spyridopoulos","doi":"10.3390/iot5020018","DOIUrl":"https://doi.org/10.3390/iot5020018","url":null,"abstract":"With the increase in the adoption of Internet of Things (IoT) devices, the security threat they face has become more pervasive. Recent research has demonstrated that most IoT devices are insecure and vulnerable to a range of cyber attacks. The impact of such attacks can vary significantly, from affecting the service of the device itself to putting their owners and their personal information at risk. As a response to improving their security, the focus has been on attacks, specifically on the network layer. However, the importance and impact of other vulnerabilities, such as low-level Radio Frequency (RF) attacks, have been neglected. Such attacks are challenging to detect, and they can be deployed using non-expensive equipment and can cause significant damage. This paper explores security vulnerabilities that target RF communications on popular commercial IoT devices such as Wi-Fi, Zigbee, and 433 Mz. Using software-defined radio, a range of attacks were deployed against the devices, including jamming, replay attacks, packet manipulation, protocol reverse engineering, and harmonic frequency attacks. The results demonstrated that all devices used were susceptible to jamming attacks, and in some cases, they were rendered inoperable and required a hard reset to function correctly again. This finding highlights the lack of protection against both intentional and unintentional jamming. In addition, all devices demonstrated that they were susceptible to replay attacks, which highlights the need for more hardened security measures. Finally, this paper discusses proposals for defence mechanisms for enhancing the security of IoT devices against the aforementioned attacks.","PeriodicalId":511512,"journal":{"name":"IoT","volume":"8 2‐3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377005","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

Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation 利用图像处理和机器学习技术进行农作物喷洒的智能农业无人机：实验验证

IoT Pub Date : 2024-05-22 DOI: 10.3390/iot5020013

Edward Singh, Aashutosh Pratap, Utkal Mehta, Sheikh Izzal Azid

{"title":"Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation","authors":"Edward Singh, Aashutosh Pratap, Utkal Mehta, Sheikh Izzal Azid","doi":"10.3390/iot5020013","DOIUrl":"https://doi.org/10.3390/iot5020013","url":null,"abstract":"Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that use machine learning techniques such as TensorFlow Lite with an EfficientDetLite1 model to identify objects from a custom dataset trained on three crop classes, namely, pineapple, papaya, and cabbage species, achieving an inference time of 91 ms. The system’s operation is characterised by its adaptability, offering two spray modes, with spray modes A and B corresponding to a 100% spray capacity and a 50% spray capacity based on real-time data, embodying the potential of Internet of Things for real-time monitoring and autonomous decision-making. The drone is operated with an X500 development kit and has a payload of 1.5 kg with a flight time of 25 min, travelling at a velocity of 7.5 m/s at a height of 2.5 m. The drone system aims to improve sustainable farming practices by optimising pesticide application and improving crop health monitoring.","PeriodicalId":511512,"journal":{"name":"IoT","volume":"51 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108792","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

FedMon: A Federated Learning Monitoring Toolkit FedMon：联合学习监控工具包

IoT Pub Date : 2024-04-11 DOI: 10.3390/iot5020012

Moysis Symeonides, Demetris Trihinas, F. Nikolaidis

{"title":"FedMon: A Federated Learning Monitoring Toolkit","authors":"Moysis Symeonides, Demetris Trihinas, F. Nikolaidis","doi":"10.3390/iot5020012","DOIUrl":"https://doi.org/10.3390/iot5020012","url":null,"abstract":"Federated learning (FL) is rapidly shaping into a key enabler for large-scale Artificial Intelligence (AI) where models are trained in a distributed fashion by several clients without sharing local and possibly sensitive data. For edge computing, sharing the computational load across multiple clients is ideal, especially when the underlying IoT and edge nodes encompass limited resource capacity. Despite its wide applicability, monitoring FL deployments comes with significant challenges. AI practitioners are required to invest a vast amount of time (and labor) in manually configuring state-of-the-art monitoring tools. This entails addressing the unique characteristics of the FL training process, including the extraction of FL-specific and system-level metrics, aligning metrics to training rounds, pinpointing performance inefficiencies, and comparing current to previous deployments. This work introduces FedMon, a toolkit designed to ease the burden of monitoring FL deployments by seamlessly integrating the probing interface with the FL deployment, automating the metric extraction, providing a rich set of system, dataset, model, and experiment-level metrics, and providing the analytic means to assess trade-offs and compare different model and training configurations.","PeriodicalId":511512,"journal":{"name":"IoT","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716122","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

Development of a Multi-Radio Device for Dry Container Monitoring and Tracking 开发用于干货集装箱监控和跟踪的多无线电设备

IoT Pub Date : 2024-04-02 DOI: 10.3390/iot5020010

Mariano Falcitelli, Misal Misal, S. Noto, Paolo Pagano

{"title":"Development of a Multi-Radio Device for Dry Container Monitoring and Tracking","authors":"Mariano Falcitelli, Misal Misal, S. Noto, Paolo Pagano","doi":"10.3390/iot5020010","DOIUrl":"https://doi.org/10.3390/iot5020010","url":null,"abstract":"Maritime shipping companies have identified continuous tracking of intermodal containers as a key tool for increasing shipment reliability and generating important economies of scale. Equipping all dry containers with an Internet-connected tracking device is a need in the global shipping market that is still waiting to be met. This paper presents the methods and tools to build and test a prototype of a Container Tracking Device (CTD) that integrates NB-IoT, BLE Mesh telecommunication and low-power consumption technologies for the massive deployment of the IoT. The work was carried out as part of a project to build the so-called “5G Global Tracking System”, enabling several different logistic applications relying on massive IoT, M2M standard platforms, as well as satellite networks to collect data from dry containers when the vessel is in open sea. Starting from a preliminary phase, in which state-of-the-art technologies, research approaches, industrial initiatives and developing standards were investigated, a prototype version of the CTD has been designed, verified and developed as the first fundamental step for subsequent industrial engineering. The results of specific tests are shown: after verifying that the firmware is capable of handling the various functions of the device, a special focus is devoted to the power consumption measurements of the CTD to size the battery pack.","PeriodicalId":511512,"journal":{"name":"IoT","volume":"315 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754434","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

Integration of Smart Cane with Social Media: Design of a New Step Counter Algorithm for Cane 智能手杖与社交媒体的整合：为手杖设计新的步进计数器算法

IoT Pub Date : 2024-03-14 DOI: 10.3390/iot5010009

Mohamed Dhiaeddine Messaoudi, Bob-Antoine J. Menelas, Hamid Mcheick

{"title":"Integration of Smart Cane with Social Media: Design of a New Step Counter Algorithm for Cane","authors":"Mohamed Dhiaeddine Messaoudi, Bob-Antoine J. Menelas, Hamid Mcheick","doi":"10.3390/iot5010009","DOIUrl":"https://doi.org/10.3390/iot5010009","url":null,"abstract":"This research introduces an innovative smart cane architecture designed to empower visually impaired individuals. Integrating advanced sensors and social media connectivity, the smart cane enhances accessibility and encourages physical activity. Three meticulously developed algorithms ensure accurate step counting, swing detection, and proximity measurement. The smart cane’s architecture comprises the platform, communications, sensors, calculation, and user interface layers, providing comprehensive assistance for visually impaired individuals. Hardware components include an audio–tactile interaction module, input command module, microphone integration, local storage, step count module, cloud integration, and rechargeable battery. Software v1.9.7 components include Facebook Chat API integration, Python Facebook API integration, fbchat library integration, and Speech Recognition library integration. Overall, the proposed smart cane offers a comprehensive solution to enhance mobility, accessibility, and social engagement for visually impaired individuals. This study represents a significant stride toward a more inclusive society, leveraging technology to create meaningful impact in the lives of those with visual impairments. By fostering socialization and independence, our smart cane not only improves mobility but also enhances the overall well-being of the visually impaired community.","PeriodicalId":511512,"journal":{"name":"IoT","volume":"77 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241988","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 Wearable Internet of Things Device for Noninvasive Remote Monitoring of Vital Signs Related to Heart Failure 用于无创远程监测心衰相关生命体征的可穿戴物联网设备

IoT Pub Date : 2024-03-12 DOI: 10.3390/iot5010008

S. M. A. Iqbal, Mary Ann Leavitt, Imadeldin Mahgoub, W. Asghar

引用次数: 0

Analyzing Threats and Attacks in Edge Data Analytics within IoT Environments 分析物联网环境中边缘数据分析的威胁和攻击

IoT Pub Date : 2024-03-05 DOI: 10.3390/iot5010007

Poornima Mahadevappa, Redhwan Al-amri, G. Alkawsi, A. Alkahtani, M. Alghenaim, Mohammed Alsamman

引用次数: 0