Internet of Things最新文献

{"title":"Visualisation of movement patterns supporting teacher reflection - qualitative analysis of educational benefits of IoT in the classroom","authors":"Patrik Hernwall,&nbsp;Robert Ramberg","doi":"10.1016/j.iot.2025.101743","DOIUrl":"10.1016/j.iot.2025.101743","url":null,"abstract":"<div><div>Research on IoT in educational contexts indicates a need for qualitative studies evaluating IoT technology use in schools. In this article, results from a qualitative study of the use of IoT technology to register and visualise teachers’ movements in the classroom, where visualisations of teachers’ movements are used to support teacher reflection on their practice, is reported. Data were collected through iterative conversations with 18 participating teachers from 9 different schools. In total 72 conversations of between 15 to 40 minutes each were carried out. In a thematic analysis, four themes emerged reflecting the benefits experienced by the teachers; movement, memory support, pupil perspective, collegial use and benefit. The results in sum indicate that visualisation of teachers’ movements support teachers to reflect on their classroom practice by providing an objective representation of movement, as a complement to subjective memory and experience.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101743"},"PeriodicalIF":7.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil moisture estimation using continuous wave radar","authors":"Hui Huang ,&nbsp;Kiyoshy Nakamura ,&nbsp;Patricia Becerra ,&nbsp;Jin Zhang ,&nbsp;Tong Hao ,&nbsp;Radu State","doi":"10.1016/j.iot.2025.101718","DOIUrl":"10.1016/j.iot.2025.101718","url":null,"abstract":"<div><div>This work introduces SoilCW, a low maintenance and cost-effective soil moisture monitoring system that uses compact and affordable continuous wave (CW) radar technology. By employing passive metal reflectors to eliminate the need for battery-powered underground sensors, SoilCW helps reduce deployment costs and minimizes the risk of soil contamination. SoilCW operates on the principle that the phase change of radar echoes is related to the moisture content of the soil through which the electromagnetic (EM) wave propagates. To accurately capture the phase changes caused by soil moisture, the SoilCW uses two auxiliary frequencies close to the main radar frequency to eliminate the impact of ground reflection and addresses the phase ambiguity that can occur due to significant variations in signal propagation velocity within the soil. This design utilizes only a narrow bandwidth and does not require an antenna array, commonly needed in existing works. The prototype of SoilCW was developed using a software-defined radio (SDR) board, and extensive evaluations were conducted in laboratory environments, with sandy soil and potting mix loam, as well as field environments with clay soil, and considered various surface coverings such as mulches and rocks. The results show that CW radars are promising for low-cost and accurate soil moisture monitoring. The overall mean absolute error is approximately 1.91% under laboratory conditions, and the results obtained from the field experiments are comparable to those of dedicated industrial grade soil sensors.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101718"},"PeriodicalIF":7.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SEACount: Semantic-driven Exemplar query Attention framework for image boosting class-agnostic Counting in Internet of Things","authors":"Xinhui Lin ,&nbsp;Mingzhu Shi ,&nbsp;Yuhao Su ,&nbsp;Wenxin Zhang ,&nbsp;Yujiao Cai ,&nbsp;Lei Liu ,&nbsp;Zhaowei Liu","doi":"10.1016/j.iot.2025.101741","DOIUrl":"10.1016/j.iot.2025.101741","url":null,"abstract":"<div><div>In the Internet of Things (IoT) environment, large amounts of visual data are continuously collected, providing a rich resource for intelligent surveillance and management. For the task of class-agnostic counting in images, this paper proposes the Semantic-driven Exemplar query Attention Counting (SEACount) framework, which aims to quickly adapt and count unseen classes of objects using a few-shot exemplars. This is critical for real-time monitoring and analyzing visual semantic information in IoT. Specifically, we introduce two new components to extend Object Detection with Transformers (DETR): the Exemplar Query Attention (EQA) and the Dynamic Reshaping Module (DRM). EQA injects exemplar queries with rich semantic information into the decoder, facilitating the global image response to exemplar targets and enhancing the exemplar-to-image similarity metrics. The DRM, instead of only utilizing decoder features, fuses them with image features to enhance local details, reduce noise interference, and reshape the feature maps required for predicting density maps. This approach efficiently captures exemplar-relevant targets in images and quickly adapts to new categories without fine-tuning. Experimental results demonstrate that our proposed SEACount framework significantly outperforms other state-of-the-art methods on the latest FSC-147 dataset. We release the code at <span><span>https://github.com/lxinhui1109/SEACount.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101741"},"PeriodicalIF":7.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PQ-BCDA: A post-quantum blockchain based cross-domain authentication scheme for Internet of Things","authors":"Shuanggen Liu ,&nbsp;Siyuan Rao ,&nbsp;Xu An Wang ,&nbsp;Kexin Tian ,&nbsp;Yue Wang","doi":"10.1016/j.iot.2025.101737","DOIUrl":"10.1016/j.iot.2025.101737","url":null,"abstract":"<div><div>The growing deployment of Internet of Things (IoT) devices across heterogeneous trust domains raises critical concerns for secure and efficient cross-domain authentication, especially under the emerging threat of quantum computing. Existing approaches often rely on centralized authorities or classical cryptographic primitives, making them vulnerable to single points of failure and future cryptanalytic advances. To address these challenges, this paper proposes PQ-BCDA, a novel post-quantum cross-domain authentication scheme that combines the Extended Merkle Signature Scheme (XMSS) with a consortium blockchain framework. Our scheme introduces an automated signature lifecycle management mechanism via smart contracts, enabling decentralized trust management and secure authentication without relying on centralized anchors. We formalize a tailored security model based on established frameworks and provide a detailed proof in the random oracle model, ensuring session key secrecy, mutual authentication, and resistance to common attacks. Experimental evaluations on real hardware platforms, demonstrate that PQ-BCDA reduces computational and storage costs by 46% and 33%, respectively.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101737"},"PeriodicalIF":7.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An open testbed for O-RAN experimentation with AI-enabled control and monitoring","authors":"Raúl Parada ,&nbsp;Xavier Vilajosana ,&nbsp;Sobhi Alfayoumi ,&nbsp;Jordi Serra ,&nbsp;Oriol Font-Bach ,&nbsp;Paolo Dini","doi":"10.1016/j.iot.2025.101729","DOIUrl":"10.1016/j.iot.2025.101729","url":null,"abstract":"<div><div>The proliferation of open radio access networks (O-RAN) in modern 5G systems has ushered in enhanced flexibility and efficiency, but it has also introduced novel security challenges. In response, this paper presents a novel AI-based anomaly detection framework tailored for O-RAN networks operating in 5G environments. By employing principal component analysis for dimensionality reduction and a deep neural network for classification, the proposed system efficiently processes large-scale 5G traffic data while achieving high detection accuracy and low latency. Experimental evaluation on an open-source testbed with realistic cellular traffic demonstrates rapid convergence, with both training and validation accuracy values approaching 100% and effective detection of anomalies introduced via user equipment identifier swaps. The testbed processed over 300,000 traffic samples with 31 distinct network features, emulating 8 unique user equipment profiles under diverse radio conditions. Under adversarial scenarios, such as identity-swapping attacks, the system identified anomalous behavior with detection rates exceeding 40%, while maintaining a near-zero false positive rate on clean traffic. These results underscore the testbed’s capability to simulate complex 5G environments and the framework’s ability to deliver highly accurate, low-latency, and scalable anomaly detection. Overall, this work highlights the potential of advanced AI techniques to significantly enhance the security and resilience of modern wireless communication networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101729"},"PeriodicalIF":7.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simulation framework for assessing and optimizing IoT service and resource allocation: SimulateIoT-Services","authors":"Fernando Díaz Cantero,&nbsp;José Ángel Barriga Corchero,&nbsp;Miguel Ángel Pérez-Toledano,&nbsp;Pedro J. Clemente","doi":"10.1016/j.iot.2025.101736","DOIUrl":"10.1016/j.iot.2025.101736","url":null,"abstract":"<div><div>The Internet of Things (IoT) is increasingly being applied across various domains, including smart cities, smart buildings, agriculture, and connected vehicles, also known as the Internet of Vehicles (IoV), as well as industry, where it is commonly referred to as the Industrial IoT (IIoT). The ultimate goal of these systems is to provide services to end users. Services can be deployed across the computing layers of IoT systems, such as the fog or cloud layers, in a process known as service allocation. Similarly, hardware resources can be configured for each node within these layers, a process referred to as resource allocation. Properly executing these processes is crucial to achieving optimal performance in IoT systems. However, these processes are complex, and no single universally accepted method exists for carrying them out. Instead, the literature contains numerous proposals aimed at optimizing system performance by refining the execution of these tasks. In this work, to provide tools for optimizing service and resource allocation, SimulateIoT, an IoT simulator based on Model-Driven Development, has been extended to support these concepts. This extension, named SimulateIoT-Services, enables users to model, validate, generate, deploy, and test IoT systems, assessing their performance and evaluating how different service and resource allocation strategies impact overall system performance. Finally, this study not only extends SimulateIoT towards SimulateIot-Services, but also showcases its practical application. Namely, in the design and evaluation of scalable IoT systems and services through a case study centered on the IoV and efficient parking within a smart city context.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101736"},"PeriodicalIF":7.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion-based congestion control method for the power internet of things combining data-driven and rule-engine models","authors":"Chunxin Wang ,&nbsp;Wenyu Qu ,&nbsp;Rui Hou ,&nbsp;Feng Jiao ,&nbsp;Ying Zou","doi":"10.1016/j.iot.2025.101738","DOIUrl":"10.1016/j.iot.2025.101738","url":null,"abstract":"<div><div>The integration of the Internet of Things (IoT) into smart grids is placing an ever-growing emphasis on achieving high transmission stability. With the massive integration of heterogeneous devices into the power IoT, the volume of transmitted data has surged, and the dynamic complexity of networks has increased, leading to prolonged data congestion due to delayed transmission. Existing congestion control algorithms are typically divided into rule-based and learning-based approaches. However, relying exclusively on a single type of algorithm can result in challenges like suboptimal bandwidth utilization or slow convergence. To enhance data transmission stability, this paper introduces a congestion control method that integrates rule-based models with data-driven models. The method integrates network temporal features extracted by the Transformer algorithm with rule-based features from Google's Congestion Control (GCC) algorithm, and employs Proximal Policy Optimization (PPO) reinforcement learning to generate real-time transmission rate control strategies. This approach improves both the efficiency and robustness of deep reinforcement learning algorithms for congestion control. The performance evaluation metrics used for comparison are Quality of Service (QoS), mainly focusing on throughput, latency, and average packet loss rate. Compared to other algorithms, this model achieves the best performance with a score of 88.13. The hybrid algorithm offers a promising direction for future network congestion control.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101738"},"PeriodicalIF":7.6,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient IoT indoor monitoring via distributed deep learning in hybrid VLC/RF network architectures","authors":"Thai-Ha Dang ,&nbsp;Ngoc-Hai Dang ,&nbsp;Viet-Thang Tran","doi":"10.1016/j.iot.2025.101715","DOIUrl":"10.1016/j.iot.2025.101715","url":null,"abstract":"<div><div>The rapid growth of the Internet of Things (IoT) has driven the demand for efficient indoor monitoring systems that can provide reliable connectivity, low latency, and high energy efficiency. Traditional wireless approaches based solely on radio frequency (RF) suffer from spectrum congestion and interference, while visible light communication (VLC) systems are constrained by line-of-sight requirements and limited coverage. To address these challenges, this paper proposes a hybrid VLC/RF network architecture integrated with distributed deep learning for IoT-based indoor monitoring. In the proposed framework, IoT devices leverage VLC links for high-speed data transmission in favorable conditions and seamlessly switch to RF links to maintain connectivity in non-line-of-sight scenarios. A distributed deep learning framework is deployed across edge nodes to enable scalable and privacy-preserving analytics, reducing reliance on centralized processing while improving adaptability to dynamic indoor environments. Experimental evaluation demonstrates that the proposed system achieves higher monitoring accuracy, reduced latency, and improved energy efficiency compared to single-mode VLC or RF systems. These findings highlight the potential of hybrid communication networks combined with distributed intelligence to enhance the performance and robustness of IoT indoor monitoring applications in smart homes, healthcare, and industrial environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101715"},"PeriodicalIF":7.6,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How intelligence is reshaping today: IoE edge networks?","authors":"Syed Usman Jamil ,&nbsp;M. Arif Khan ,&nbsp;M.A. Rahman ,&nbsp;Muhammad Ali Paracha ,&nbsp;Tanveer Zia ,&nbsp;Syed Sadiqur Rahman ,&nbsp;Syed Bilal Ahmed","doi":"10.1016/j.iot.2025.101717","DOIUrl":"10.1016/j.iot.2025.101717","url":null,"abstract":"<div><div>The rapid advancement of Internet of Everything (IoE) devices, coupled with emergent communication technologies such as Sixth Generation (6G), is facilitating the development of IoE-based edge networks wherein services are rendered closer to the network periphery. In these networks, devices exchange information and share computational resources. This research paper presents a novel methodological framework designed to optimise task allocation and device selection in IoE-6G systems. By integrating intelligent algorithms, our study investigates the impact of device selection delay, computational efficiency, and task success rates on overall system performance. The unique capabilities of 6G, such as AI-native infrastructure, Ultra-Reliable Low-Latency Communication (URLLC), Massive Machine-Type Communication (mMTC), Terahertz (THz) frequency bands, and dynamic network slicing, form the foundational enablers of our proposed framework. These features are essential for scalable and real-time IoE operations and are tightly integrated into our proposed system’s algorithmic design. Our methodological framework involves extensive simulations that evaluate the proposed system across various scenarios, focusing on foundational concepts and performance metrics that are essential for understanding the parameters influencing our research outcomes. We provide a detailed comparison of traditional and intelligent scheduling algorithms, showcasing significant improvements in task allocation and completion times when intelligence is employed. The novelty of Intelligent Main Task Off-loading Scheduling Algorithm (<em>i</em>MTOSA) lies in its dynamic intelligence and adaptive scheduling, optimising IoE task offloading with minimal communication and enhanced scalability by focusing on advanced groups of metrics and thoroughly discussing the implications for real-world IoE-6G environments. Our results contribute to a deeper understanding of the integration of intelligent systems in modern communication networks, paving the way for future advancements in IoE technologies. In overall performance, the intelligent variants of the proposed algorithm show less than 50% affected tasks, while non-intelligent scheduling algorithms exceed 90% affected tasks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101717"},"PeriodicalIF":7.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable three-way neural network approach for intrusion detection in IoT ecosystem","authors":"Fazal Wahab ,&nbsp;Shengjun Ma ,&nbsp;Yuhai Zhao ,&nbsp;Anwar Shah","doi":"10.1016/j.iot.2025.101722","DOIUrl":"10.1016/j.iot.2025.101722","url":null,"abstract":"<div><div>Deep neural networks (DNNs) have demonstrated remarkable potential in intrusion detection; however, the inherent uncertainty in the data often results in false alerts due to the deterministic nature of their probabilistic classification mechanism. In IoT environments, this can affect system reliability. Moreover, most of the DNN-based methods do not provide a transparent and interpretable output of the model. A secure, reliable, and accurate framework is urgently required to protect IoT systems. To fill this gap, this article introduces a novel, explainable three-way neural network approach called Ex3WNN. We introduce a 3WC mechanism in the final layer of the DNN using Game-theoretic Rough Sets (GTRS). This allows the model to handle uncertain cases more intelligently by distinguishing between confident decisions and those requiring further interpretation. By categorizing predictions into attack, suspicious (uncertain), and normal classes, 3WC helps manage uncertainty, ensuring that ambiguous cases are flagged for further analysis rather than misclassified. The GTRS framework is employed to determine optimal decision thresholds, which are derived while achieving the trade-off between generality and accuracy. This approach enhances both detection accuracy and reliability. Incorporating the suspicious region can considerably reduce false alerts and significantly enhance the reliability, security, and confidence of the intrusion detection system (IDS). Furthermore, we utilize the eXplainable AI (XAI) techniques to provide an interpretable and transparent model’s output. The experimental results from four relevant and comprehensive datasets show that the proposed method outperformed existing baselines.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101722"},"PeriodicalIF":7.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}