Internet of Things最新文献

{"title":"6G Internet-of-Things assisted smart homes and buildings: Enabling technologies, opportunities and challenges","authors":"Arif Ullah ,&nbsp;Fawad ,&nbsp;Aamir Nadeem ,&nbsp;Muhammad Arif ,&nbsp;Muhammad Mehran Bashir ,&nbsp;Wooyeol Choi","doi":"10.1016/j.iot.2025.101658","DOIUrl":"10.1016/j.iot.2025.101658","url":null,"abstract":"<div><div>Smart homes (SHs) and smart buildings (SBs) play a crucial role in addressing global urbanization and are key elements of smart cities (SCs). They utilize integrated systems to process large volumes of data, enabling intelligent responses and improved quality of life. However, the growing number of connected devices and increasing data demands have exceeded the capabilities of existing network technologies, reducing service effectiveness. This article provides a comprehensive survey of emerging technologies, especially those underexplored in SH and SB contexts. It highlights the potential of sixth-generation (6G) wireless networks and the Internet of Things (IoT) to transform smart indoor environments through greater automation, intelligence, and adaptability. The authors first analyze the limitations of current IoT-based systems and then examine advanced 6G technologies, including integrated sensing and communication (ISAC), machine learning (ML), visible light communication (VLC), reconfigurable intelligent surfaces (RIS), and blockchain (BC). These technologies aim to resolve issues related to scalability, connectivity, and interoperability that challenge current IoT/5G models. Unlike previous studies focusing on narrow areas such as energy or security, this survey takes a holistic approach. It aligns 6G capabilities with SH/SB needs like human-device interaction, energy efficiency, and robust security, and introduces new applications such as AI-driven digital twins and edge intelligence. The article also identifies research gaps, including resource allocation in diverse networks and ethical AI deployment. It offers guidance for future research and urges collaboration, standardization, and human-centric design to enable next-generation intelligent living environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101658"},"PeriodicalIF":6.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231788","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 algorithm to restructure the Semi-Homogeneous Tree-Based Fog Computing (SHTBFC) model to reduce the energy consumption","authors":"Dilawaer Duolikun ,&nbsp;Tomoya Enokido ,&nbsp;Makoto Takizawa","doi":"10.1016/j.iot.2025.101655","DOIUrl":"10.1016/j.iot.2025.101655","url":null,"abstract":"<div><div>In the IoT (Internet of Things), a large amount of electric energy is consumed since a tremendous volume of sensor data is transmitted from devices to servers in networks and large computation resources are spent to execute sensor applications on servers. In order to realize the energy-efficient IoT, we discuss an <span><math><mrow><mi>S</mi><mi>H</mi><mi>T</mi><mi>B</mi><mi>F</mi><mi>C</mi></mrow></math></span> (Semi-Homogeneous Tree-Based Fog Computing) model where types of servers and devices are interconnected in a tree of homogeneous fog nodes. Here, a root stands for a server and a leaf indicates a device. Thus, all the nodes are not homogeneous in an SHTBFC model. Each fog node generates output data by processing input data from the child fog nodes and passes the output data to the parent node. If some fog node <span><math><mi>f</mi></math></span> does not behave correctly due to faults, the descendant fog nodes of <span><math><mi>f</mi></math></span> cannot communicate with the ancestor nodes. In order to recover from the faults, improve the performance, and reduce the energy consumption, we newly propose a <span><math><mrow><mi>T</mi><mi>R</mi></mrow></math></span> (Tree-Restructuring) algorithm to restructure an SHTBFC tree to an SHTBFC tree which consumes smaller energy. An objective fog node is first taken, which consumes the largest energy in an <span><math><mrow><mi>S</mi><mi>H</mi><mi>T</mi><mi>B</mi><mi>F</mi><mi>C</mi></mrow></math></span> tree. Then, one of three <span><math><mrow><mi>T</mi><mi>R</mi></mrow></math></span> operations, <span><math><mi>M</mi></math></span> (migrate), <span><math><mi>R</mi></math></span> (replicate), and <span><math><mi>X</mi></math></span> (expand) operations is executed on the objective node. The energy consumption of an application depends on the number of application processes. The more processes an application is composed of, the more fog nodes are included in an SHTBFC tree obtained by the <span><math><mrow><mi>T</mi><mi>R</mi></mrow></math></span> algorithm. In the evaluation, we show the number of application processes where an application consumes the smallest energy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101655"},"PeriodicalIF":6.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231787","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":"Adversarial attacks on artificial Intelligence of Things-based operational technologies in theme parks","authors":"Sadaf Hina ,&nbsp;Qaiser Abbas ,&nbsp;Kashan Ahmed","doi":"10.1016/j.iot.2025.101654","DOIUrl":"10.1016/j.iot.2025.101654","url":null,"abstract":"<div><div>Theme parks represent a popular, yet vulnerable aspect of life, where large unsuspecting crowds gather and interact with technology. Artificial intelligence (AI), computer vision, and the Internet of Things (IoT) are transforming theme parks by revolutionizing various aspects. This research study is the first to identify critical components of theme parks that can be optimized, and comprehensively maps them onto emerging AI/IoT applications, often powered by machine learning or deep learning models. Additionally, the study sheds light on adversarial attacks targeting vulnerable smart surveillance systems, which generate a very large volume of video stream data. These systems serve as a prominent example of AIoT-based operational technologies (AIoT-OT) responsible for critical alerts and actions. Rigorous experimentation, involving a novel hybrid multi-pixel deception attack technique, demonstrates that advanced adversarial attack methods can significantly degrade the performance of detection systems. The performance metrics and attack success rate were measured by accuracy, precision, recall, F1-score, and AUC score. Before attack, the accuracy rates of 87. 45%, 83. 17% and 81. 40% were achieved for the EfficientNet, ResNet and MobileNet models, respectively. However, after applying the proposed MPD attack, the performance of each model declined significantly. The accuracy dropped to 61.23% for EfficientNet (with an attack success rate of 29.10%), 59.12% for ResNet (with success rate of 30.20%), and 55.17% for MobileNet (with success rate of 32.60%). This study signifies the need for a strategic plan of action and the development of robust methods for the proactive security of AIoT in theme parks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101654"},"PeriodicalIF":6.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204988","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":"DSEAGC: Dual-spectral embedding for attributed graph clustering","authors":"Yuwen Zhao ,&nbsp;Weifang Liang ,&nbsp;Zhi Gong ,&nbsp;Shibing Sun ,&nbsp;Shayan Nejadshamsi","doi":"10.1016/j.iot.2025.101651","DOIUrl":"10.1016/j.iot.2025.101651","url":null,"abstract":"<div><div>Community detection in node-attributed networks, where nodes are characterized by both structural connections and attribute information, is a crucial task in network analysis. Accurately identifying these communities reveals underlying patterns and relationships, offering deeper insights into the network’s structure and behavior. However, effectively integrating structural and attribute data remains a challenge. To address this, we introduce a novel framework called DSEAGC (Dual-Spectral Embedding for Attributed Graph Clustering), which jointly leverages structure and attribute spaces through a three-stage spectral learning pipeline. Specifically, DSEAGC constructs separate Laplacian matrices for both graph topology and node attributes, performs independent spectral embeddings, and fuses these views using an adaptive objective function. An iterative optimization technique balances the preservation of structural and attribute information while achieving discrete cluster assignments via spectral rotation. Unlike existing methods, DSEAGC incorporates a dual preservation mechanism and rotation-based discretization to enhance cluster separability. This unified representation enhances community detection by reflecting structural and attribute similarities and capturing both complementary and consensus information. These improvements are evidenced through comprehensive evaluations using multiple clustering metrics, validating the effectiveness and scalability of DSEAGC in practical attributed network scenarios.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101651"},"PeriodicalIF":6.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230361","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":"Digital Twin and sustainability: A data-driven scientometric exploration","authors":"Munish Bhatia,&nbsp;Rohit Kumar","doi":"10.1016/j.iot.2025.101652","DOIUrl":"10.1016/j.iot.2025.101652","url":null,"abstract":"<div><div>Digital Twin (DT) technology has emerged as a transformative innovation, enabling the creation of precise digital replicas of real-world systems, processes, and objects. By establishing a seamless connection between the physical and digital realms, DTs facilitate real-time data integration, advanced simulations, and optimization processes. This capability significantly enhances decision-making, predictive maintenance, and operational efficiency across diverse industries. The adoption of DT technology has accelerated rapidly, with sectors such as agriculture, healthcare, energy, and transportation leading its implementation. The present study employs scientometric analysis to evaluate the impact and efficacy of DT models in advancing the United Nations’ Sustainable Development Goals (SDGs). Utilizing cutting-edge tools such as CiteSpace and VOSviewer for network analysis, the research leverages data sourced from the Scopus database, encompassing publications from 2018 to 2024. The analysis examines key dimensions, including publication trends, citation dynamics, keyword co-occurrence networks, document co-citation patterns, country-level collaboration, and author co-citation networks. The study identifies influential publications, prominent researchers, and leading nations contributing to the evolution of DT technology, highlighting critical innovations and contributions. These insights not only provide a comprehensive understanding of the current state of DT technology in the context of sustainable development but also reveal emerging research directions and trends. Furthermore, the findings underscore the potential for interdisciplinary collaboration to advance the role of DT technology in achieving the SDGs, paving the way for future advancements in the domain.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101652"},"PeriodicalIF":6.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190433","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":"Zero-knowledge machine learning models for blockchain peer-to-peer energy trading","authors":"Caixiang Fan ,&nbsp;Amirhossein Sohrabbeig ,&nbsp;Petr Musilek","doi":"10.1016/j.iot.2025.101638","DOIUrl":"10.1016/j.iot.2025.101638","url":null,"abstract":"<div><div>Blockchain-based peer-to-peer energy trading enables individuals to directly share renewable energy using Internet of Things technologies. However, it faces significant challenges related to privacy, scalability, and the integration of advanced artificial intelligence. To address these issues, this article proposes zkPET, a secure and intelligent peer-to-peer energy trading framework. zkPET integrates machine learning and blockchain with advanced cryptographic techniques of zero-knowledge machine learning to protect user data while enabling intelligent decision making. In the zkPET framework, the computationally intensive operations of various machine learning models are executed off-chain, and only succinct cryptographic proofs of these computations are uploaded to the blockchain for verification and recording. In addition, a time-series clustering approach is incorporated into federated learning to enhance both inference accuracy and the efficiency of proof generation. Experimental validation using the zero-knowledge proof tool EZKL and a real-world electricity dataset demonstrates the feasibility and effectiveness of zkPET. The results underscore its potential to significantly improve privacy, scalability, and computational efficiency in decentralized energy trading, contributing to the advancement of secure and intelligent energy markets.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101638"},"PeriodicalIF":6.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169727","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":"Collaborative edge learning-assisted interactive avatar generation framework for IoT-driven metaverse","authors":"Ahmad Zainudin ,&nbsp;Made Adi Paramartha Putra ,&nbsp;Dong-Seong Kim ,&nbsp;Jae-Min Lee","doi":"10.1016/j.iot.2025.101639","DOIUrl":"10.1016/j.iot.2025.101639","url":null,"abstract":"<div><div>The Beyond 5G (B5G) wireless networks offer massive Internet of Things (IoT) integration with metaverse services, leveraging high data rates and low latency capabilities. Avatars are essential components in the metaverse platform. Creating an interactive avatar that utilizes IoT-based human pose estimation by utilizing centralized deep learning (DL) poses significant challenges. Federated learning (FL) offers a solution by enabling local training on edge devices and collaboratively producing a reliable model. This study proposes edge learning-assisted human activity recognition (HAR) using the FL technique and integrates it with an IoT-driven intelligent metaverse platform to create an interactive avatar. The HAR framework captures human gestures using infrared array sensor devices and recognizes activities with a lightweight hybrid model called AvatarNet. An enhanced data distribution and reputation-aware (iDDR) client selection scenario is implemented to identify potential clients and improve model performance. Furthermore, a connection module based on JavaScript and the WebSocket protocol has been developed to integrate the HAR framework with the Unreal Engine (UE) metaverse platform. The proposed model was tested using our infrared-based HAR and public datasets, outperforming state-of-the-art in accuracy and model complexity. The measurements show that the proposed model achieves an accuracy of 96.49%, precision of 94.84%, recall of 94.78%, F1 score of 94.80%, and MFLOPs calculation of 0.0431.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101639"},"PeriodicalIF":6.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195321","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 lightweight hybrid approach for intrusion detection systems using a chi-square feature selection approach in IoT","authors":"Hafsa Benaddi ,&nbsp;Mohammed Jouhari ,&nbsp;Omar Elharrouss","doi":"10.1016/j.iot.2025.101624","DOIUrl":"10.1016/j.iot.2025.101624","url":null,"abstract":"<div><div>Protecting IoT networks from cyber threats is challenging, especially with resource-constrained devices. This paper proposes an efficient, lightweight hybrid intrusion detection system (IDS) specifically optimized for IoT devices. Our innovative approach integrates convolutional neural networks (CNN) for effective spatial feature extraction and bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies. Crucially, we employ a chi-square (<span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) feature selection method, significantly reducing input complexity by selecting the 20 most relevant features from the UNSW-NB15 dataset. Benchmarking against recent IDS methods, our model achieved outstanding accuracy: 97.90% for binary classification and 97.09% for multiclass scenarios, clearly outperforming existing approaches. Additionally, computational performance evaluation reveals rapid prediction times (1.1 s binary; 2.10 s multiclass), demonstrating suitability for real-time IoT deployment. This study illustrates a balanced trade-off between high accuracy and low computational demand, highlighting the practical benefits of advanced, resource-efficient IDS solutions for IoT security.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101624"},"PeriodicalIF":6.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169776","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":"Next-generation smart homes: CO2 monitoring with Matter protocol to support indoor air quality","authors":"Afonso Mota ,&nbsp;Carlos Serôdio ,&nbsp;Ana Briga-Sá ,&nbsp;Antonio Valente","doi":"10.1016/j.iot.2025.101649","DOIUrl":"10.1016/j.iot.2025.101649","url":null,"abstract":"<div><div>Humans spend most of their time indoors, where air quality and comfort are crucial to health and well-being. Elevated CO₂ levels in buildings can reduce cognitive function, discomfort, and health issues. Indoor CO₂ monitoring has emerged as a key focus in the literature, particularly in residential buildings, as it can play a vital role in helping to maintain adequate ventilation rates. The growing smart home market demands seamless integration and control, which are essential for implementing IAQ sensing devices. However, interoperability barriers between platforms and devices continue to hinder smart home adoption. To address these challenges, Matter protocol is starting to appear in the market. In this work, a wireless CO₂ sensor is developed based on ESP32-C6 and SCD40 and integrated into a created Matter-enabled ecosystem formed with the Home Assistant open-source platform. The utilized hardware and software enable the usage of two different wireless communication technologies, WiFi and Thread, enhancing compatibility. The study highlights the rapid and seamless onboarding of the developed CO₂ monitoring device into smart home ecosystems using the Matter protocol. As a result, once the device is successfully added to the ecosystem, the measurements can be accessed and analyzed through a mobile application, forming an IoT environment.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101649"},"PeriodicalIF":6.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231783","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":"Enhancing medical digital twins within metaverse using blockchain, NFTs and LLMs","authors":"Ruba Islayem ,&nbsp;Ahmad Musamih ,&nbsp;Khaled Salah ,&nbsp;Raja Jayaraman ,&nbsp;Ibrar Yaqoob","doi":"10.1016/j.iot.2025.101648","DOIUrl":"10.1016/j.iot.2025.101648","url":null,"abstract":"<div><div>Medical digital twins (MDTs) are rapidly emerging as transformative tools in healthcare. They provide virtual representations of medical devices and systems that facilitate real-time analysis and enhance decision-making. However, challenges such as secure data management, access control, and the lack of immersive and intelligent patient interactions limit their effectiveness. In this paper, we propose a solution integrating blockchain technology, Non-Fungible Tokens (NFTs), and Large Language Models (LLMs) within a metaverse environment to enhance MDT functionality. Blockchain and NFTs ensure secure ownership and access control, while the metaverse offers an engaging platform for user interaction. An LLM-powered non-player character (NPC) enables intelligent real-time user interactions and personalized insights. We develop two blockchain smart contracts for user registration, NFT ownership, and access control, and utilize decentralized InterPlanetary File System (IPFS) storage for the metaverse, MDT metadata, and interaction logs. We present the system architecture, sequence diagrams, and algorithms, along with the implementation and testing details. We conduct cost, security, and response time analyses to evaluate the smart contracts and LLM performance and compare our solution with existing approaches. We discuss practical implications, as well as challenges and limitations of the proposed solution. Finally, we explore the generalization of our system for various applications. The smart contract code and metaverse files are publicly available on GitHub.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101648"},"PeriodicalIF":6.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154536","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}