Internet of Things最新文献

{"title":"A contactless method for recognition of daily living activities for older adults based on ambient assisted living technology","authors":"Kang Wang ,&nbsp;Ashish Saragadam ,&nbsp;Jasleen Kaur ,&nbsp;Ayan Dogra ,&nbsp;Shi Cao ,&nbsp;Moojan Ghafurian ,&nbsp;Zahid A. Butt ,&nbsp;Shahabeddin Abhari ,&nbsp;Dmytro Chumachenko ,&nbsp;Plinio P. Morita","doi":"10.1016/j.iot.2025.101502","DOIUrl":"10.1016/j.iot.2025.101502","url":null,"abstract":"<div><h3>Background</h3><div>During demographic shifts towards an older population, healthcare systems face increased demands, highlighting the need for innovative approaches that facilitate supporting older adults’ well-being and safety. This study aims to demonstrate the effectiveness of zero-effort Ambient Assisted Living technology in recognizing daily activities of older adults via machine learning algorithms by comparing with wearable technology.</div></div><div><h3>Methods</h3><div>Conducted in a smart home environment equipped with a comprehensive range of non-intrusive sensors, the study involved 40 participants, during which they were instructed to perform 23 types of predefined daily living activities, organized in five phases. Data from these activities were concurrently captured by both ambient and wearable sensors. Analysis was performed using five machine learning models: K-Nearest Neighbors, Decision Trees, Random Forest, Adaptive Boosting, and Gaussian Naive Bayes.</div></div><div><h3>Results</h3><div>Ambient sensors, especially using the AdaBoost model, demonstrated high accuracy (0.964) in activity recognition, significantly outperforming wearable sensors (best accuracy 0.367 with Random Forest). When fusing data from both sensor types, the accuracy slightly decreases to 0.909. Despite spatial overlap challenges, ambient sensors accurately recognize activities across various room settings with accuracies all above 0.950. Feature importance analysis reveals that climatic, electrical, and motion-related features are crucial for model classification.</div></div><div><h3>Conclusion</h3><div>This study showcases the efficacy of Ambient Assisted Living technology in recognizing daily indoor activities of older adults. These findings have implications for public health, highlighting Ambient Assisted Living technology's potential to support older adults' independence and well-being, offering a promising direction for future research and application in smart living environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101502"},"PeriodicalIF":6.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight privacy-protection RFID protocol for IoT environment","authors":"Kuo-Yu Tsai,&nbsp;You-Lin Wei,&nbsp;Po-Shen Chi","doi":"10.1016/j.iot.2025.101490","DOIUrl":"10.1016/j.iot.2025.101490","url":null,"abstract":"<div><div>The rapid growth of the Internet of Things (IoT for short) has expanded its applications across diverse domains, including smart healthcare, smart homes, and smart factories. Among the key technologies driving this evolution, Radio Frequency Identification (RFID for short) plays a pivotal role in IoT ecosystems due to its automation, identity recognition, and portability attributes. These features make RFID essential for simplifying device management and enhancing traceability in practical scenarios, particularly in healthcare, where it optimizes the management of patient medical records. However, frequent information exchanges within RFID systems pose a significant challenge, as inadequate authentication mechanisms can lead to unintended exposure of sensitive personal data. Fan <em>et al</em>. propose a lightweight RFID authentication protocol in IEEE Transactions on Industrial Informatics to address this issue. Unfortunately, our analysis finds several security vulnerabilities in their protocol, including susceptibility to impersonation, traceability, and secret disclosure attacks. In this paper, we develop a new lightweight privacy-protection RFID protocol, building upon Fan <em>et al</em>.’s framework. Our security evaluation demonstrates that the proposed protocol effectively mitigates these threats, ensuring the confidentiality and integrity of sensitive data in RFID-enabled systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101490"},"PeriodicalIF":6.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222783","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":"The application of hybrid spider monkey optimization and fuzzy self-defense algorithms for multi-objective scientific workflow scheduling in cloud computing","authors":"Mustafa Ibrahim Khaleel","doi":"10.1016/j.iot.2025.101517","DOIUrl":"10.1016/j.iot.2025.101517","url":null,"abstract":"<div><div>Scheduling workflows in this cloud computing era might as well be the way to go, given that resource allocation will be significantly improved, besides reduced execution time and costs. Most conventional scheduling algorithms lack the potential for optimal performance among conflicting objectives like performance, cost-efficiency, and resource utilization. The paper proposes a new multi-objective workflow scheduling framework, where the Spider Monkey Optimization algorithm will be combined with the Fuzzy Self-Defense Algorithm. SMO algorithm emulates the foraging behavior of spider monkeys for a compelling exploration of the complex solution space to find superior task-resource mappings. Besides this, a fuzzy self-defense strategy tackles the inherent uncertainties of dynamic cloud environments to make the framework more adaptable and resilient against failures and performance degradation. The proposed framework will be multi-objective, including the optimization of minimizing execution time, optimization of resource utilization, and energy consumption. Therefore, the model will significantly improve the balance of those competing goals, drawing strengths from SMO and fuzzy logic. The effectiveness is further validated through extensive experiments using synthetic and real-world workflow applications in a simulated cloud environment. Indeed, notable improvements have been observed along all the key performance indicators related to execution time, energy efficiency, and resource utilization. Besides, the hybrid framework is much more scalable and flexible in handling massive workflows, establishing its value as a practical resource management solution in cloud computing.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101517"},"PeriodicalIF":6.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223138","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":"Lightweight anomaly detection in federated learning via separable convolution and convergence acceleration","authors":"Bin Jiang ,&nbsp;Guangfeng Wang ,&nbsp;Xuerong Cui ,&nbsp;Fei Luo ,&nbsp;Jian Wang","doi":"10.1016/j.iot.2025.101518","DOIUrl":"10.1016/j.iot.2025.101518","url":null,"abstract":"<div><div>The limitations of traditional centralized anomaly detection have led to the rise of distributed detection technology, which has become a hot topic in recent years. However, many key issues in distributed computing have attracted more attentions, such as large-scale data collection without exposing sensitive information and shared model exchange problems. Existing models have a large structure which have high requirements for edge device configuration. In this article, we propose a lightweight anomaly detection model based on federated learning, which ensures detection efficiency while protecting data privacy. In the local model, we incorporate depthwise separable convolution (DSC) into the Transformer to enhance both local and global information exchange within the window, and remove unnecessary modules to reduce the number of network structure parameters while ensuring accuracy. In the global model, we introduce a new parameter to the local client objective function to adjust the local training direction, which accelerates convergence speed and reduce client–server communication. Finally, we conducted comparative experiments on several public datasets, and the experimental results showed that our model ensures detection accuracy on par with the baseline while significantly reducing local optimization parameters. Meanwhile, after global optimization, the number of rounds required for model convergence is significantly reduced, reducing communication volume while ensuring detection accuracy. In general, the performance of our model was significantly improved compared to baseline.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101518"},"PeriodicalIF":6.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222792","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":"Exploring machine learning methods for the identification of production cycles and anomaly detection","authors":"Gabriel Iuhasz ,&nbsp;Teodor-Florin Fortiş ,&nbsp;Silviu Panica","doi":"10.1016/j.iot.2025.101508","DOIUrl":"10.1016/j.iot.2025.101508","url":null,"abstract":"<div><div>This paper presents a distributed platform that collects and processes data streams from Industrial Internet of Things/Cyber–Physical Systems. First we focus on the platform’s architecture, which enables seamless integration with various data sources and supports near-real-time processing and the performance of Machine Learning based methods for detection and analysis. The platform design is optimized for both accuracy and scalability, allowing it to handle large volumes of data efficiently. We demonstrate the design and predictive performance of our platform and methods with an emphasis on accuracy and scalability in three distinct phases: production cycle detection; detected cycle analysis and identification; and anomaly detection and root cause analysis for predictive maintenance. Our results highlight the potential of our platform and the value it can bring to operational decision-making in an industrial setting.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101508"},"PeriodicalIF":6.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoV-TwinChain: Predictive maintenance of vehicles in internet of vehicles through digital twin and blockchain","authors":"Mubashar Iqbal ,&nbsp;Sabah Suhail ,&nbsp;Raimundas Matulevičius ,&nbsp;Faiz Ali Shah ,&nbsp;Saif Ur Rehman Malik ,&nbsp;Kieran McLaughlin","doi":"10.1016/j.iot.2025.101514","DOIUrl":"10.1016/j.iot.2025.101514","url":null,"abstract":"<div><div>Vehicular networks are experiencing a significant transformation driven by integrating connected vehicles and Intelligent Transportation Systems (ITS). The Internet of Vehicles (IoV) is a rapidly evolving domain within ITS that connects vehicles, infrastructure, and smart devices to facilitate seamless data exchange. This data encompasses vital information, and by leveraging it, vehicles can make informed decisions and adapt to real-time situations. For instance, traditional vehicle maintenance practices often rely on reactive approaches, addressing issues after failures occur, which can lead to safety risks, costly repairs, and disruptions. Thus, there is a pressing need for proactive solutions to identify vehicle failures before they escalate. Accordingly, we propose the IoV-TwinChain framework integrating Digital Twin (DT), Machine Learning (ML), and blockchain for performing predictive maintenance of vehicles in the IoV by monitoring vehicle operating conditions to prevent road breakdowns and failures. DT provides real-time monitoring of vehicle operating conditions, while ML facilitates data-driven predictions for the predictive maintenance of vehicles. The IoV-TwinChain framework utilizes blockchain for data integrity and traceability within physical and twin environments. We implement a Proof of Concept (PoC) of the IoV-TwinChain framework using Microsoft Azure DT, ML models such as Random Forest and XGBoost, and Ethereum blockchain. Additionally, we formally verify the IoV-TwinChain framework correctness using High-Level Petri Nets and Bounded Model-Checking methods. With our PoC implementation and formal verification, we demonstrate that the IoV-TwinChain framework effectively enhances the predictive maintenance capabilities of vehicles in the IoV and ensures the reliability and accuracy of the system.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101514"},"PeriodicalIF":6.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223139","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":"Supporting constrained devices and networks in the decentralised Internet of Things","authors":"Ryan Kurte,&nbsp;Zoran Salcic,&nbsp;Kevin I-Kai Wang","doi":"10.1016/j.iot.2025.101496","DOIUrl":"10.1016/j.iot.2025.101496","url":null,"abstract":"<div><div>The ongoing growth of the Internet of Things (IoT) offers great opportunities to improve the way we understand and interact with the physical world. However, existing approaches face a number of challenges from providing scalable and reliable services to users to addressing the need for security and privacy. We believe decentralised architectures offer a valuable alternative to centralised approaches. However, increased performance and communication requirements render existing decentralised technologies unsuitable for use with the constrained edge- and end-devices popular in the IoT. In this paper we address these limitations. First by extending DSF-IoT with a novel mechanism for delegation to enable transparent interactions with and between IoT services on constrained and unconstrained end-devices without the trust and privacy boundaries of existing approaches. Second, delegation is demonstrated through the creation of what we believe to be the first real-world heterogeneous decentralised IoT network spanning WSAN connected microcontrollers, Arm-based single board computers, and unconstrained x64 devices. Third, we qualify DSF-IoT for use on constrained edge-devices through the development of a constrained gateway prototype and test-bench for measuring the performance of IoT brokers under varying operating conditions. This demonstrates that DSF-IoT offers equivalent (or improved) performance to popular CoAP and MQTT brokers on constrained edge-devices while providing historical data storage and access as well as end-to-end trust and privacy. Finally we perform a functional comparison of DSF-IoT against existing protocols and research to highlight the advantages of our approach and the suitability of DSF-IoTs and delegation for use with constrained edge- and end-devices to enable the creation of a truly heterogeneous future decentralised Internet of Things.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101496"},"PeriodicalIF":6.0,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure and Efficient Lightweight Authentication Protocol (SELAP) for multi-sector IoT applications","authors":"Alireza Javadi ,&nbsp;Sadegh Sadeghi ,&nbsp;Peyman Pahlevani ,&nbsp;Nasour Bagheri ,&nbsp;Samad Rostampour ,&nbsp;Ygal Bendavid","doi":"10.1016/j.iot.2025.101499","DOIUrl":"10.1016/j.iot.2025.101499","url":null,"abstract":"<div><div>The growing use of Internet of Things (IoT) systems in daily life highlights the critical need for robust IoT security. Recent efforts have focused on developing methods to improve security, leading to many published studies. IoT systems face challenges beyond security, including energy efficiency and communication bandwidth, making security design complex. This article reviews recent authentication protocols, with a detailed analysis of one called ELWSCAS, exposing its vulnerabilities. We propose a Secure and Efficient Lightweight Authentication Protocol (SELAP) to address ELWSCAS’s security flaws. Using two Raspberry Pis, we measure SELAP’s computation and communication costs, achieving 422 ms and 960 bits compared to ELWSCAS’s 548 ms and 2048 bits. Simulations with NS3 further validate SELAP’s performance, showing 276 successful authentications in 30 s, compared to ELWSCAS’s 202, with 17% less delay. These results demonstrate SELAP’s suitability for multi-sector IoT environments with varying conditions and high device density, delivering reliable performance.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101499"},"PeriodicalIF":6.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223135","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":"Creating interpretable synthetic time series for enhancing the design and implementation of Internet of Things (IoT) solutions","authors":"Dimitris Gkoulis","doi":"10.1016/j.iot.2025.101500","DOIUrl":"10.1016/j.iot.2025.101500","url":null,"abstract":"<div><div>This study establishes a foundation for addressing the challenge of developing Internet of Things (IoT) solutions in the absence of real-world data, a common obstacle in the early stages of IoT design, prototyping, and testing. Motivated by the need for reliable and interpretable synthetic data, this work introduces a structured approach and a dedicated library for creating realistic time series data. The methodology emphasizes flexibility and modularity, allowing for the combination of distinct components–such as trends, seasonality, and noise–to create synthetic data that accurately reflects real-world phenomena while maintaining interpretability. The approach’s utility is demonstrated by creating synthetic air temperature time series, which are rigorously compared against real-world datasets to assess their fidelity. The results validate the proposed methodology’s and library’s effectiveness in producing data that closely mirrors real-world patterns, providing a robust tool for IoT development in data-constrained environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101500"},"PeriodicalIF":6.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222928","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":"Two-stage Stackelberg game based physical reality sensing incentive mechanism in metaverse","authors":"Nan Zhao ,&nbsp;Lang Wan ,&nbsp;Juan Wang ,&nbsp;Zhe Chen ,&nbsp;Dejun Li ,&nbsp;Minyang Liu ,&nbsp;Ruifeng Pan","doi":"10.1016/j.iot.2025.101507","DOIUrl":"10.1016/j.iot.2025.101507","url":null,"abstract":"<div><div>With the development of digital twin technology, the concept of the metaverse has come into the limelight. How to motivate mobile users to collect physical reality sensing data to build the metaverse has become the focus of research. In this paper, a physical reality incentive mechanism is proposed based on two-stage Stackelberg game. Considering the competitive relationship between mobile users, a multi-leader multi-follower Stackelberg game model is constructed to describe the interaction between mobile users and virtual service providers (VSPs). In order to maximize the utilities of mobile users and VSPs, a unique Stackelberg equilibrium solution is derived to obtain the optimal incentive strategy. Simulation results show that our proposed incentive method is effective in motivating mobile users to participate in the physical reality sensing tasks. The overall social welfare can be increased in the metaverse physical reality sensing networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101507"},"PeriodicalIF":6.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223140","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}