Internet of Things最新文献

{"title":"OASIS: Online adaptive ensembles for drift adaptation on evolving IoT data streams","authors":"T. Anithakumari,&nbsp;Sanket Mishra","doi":"10.1016/j.iot.2025.101545","DOIUrl":"10.1016/j.iot.2025.101545","url":null,"abstract":"<div><div>In this work, our proposed OASIS framework utilizes adaptive ensembles to accommodate IoT data drift. In this work, we introduce an innovative sliding window approach using periodograms, engineered to efficiently feed models with data input. Six distinct online learners, alongside three drift adaptation algorithms: EDDM, HDDM-A and ADWIN have been tested using various feature selection methods, such as particle swarm optimization (PSO), dragonfly optimization (DA), grey wolf optimization (GWO), genetic algorithm (GA), and whale optimization algorithm (WOA), which have been carried out to validate the efficacy of the OASIS framework. We introduce a weighted probability approach derived from multiclass outcomes to ascertain the most suitable learners for leverage bagging or voting ensemble application. This is followed by an optimal scoring mechanism to determine the best training set based on accuracy and execution time criteria. The selection of models is guided by a probability-based algorithm coupled with a scoring system. Furthermore, we benchmark three state-of-the-art drift adaptation frameworks to evaluate their performance relative to our proposed framework. Evaluations in the context of EDGE-IIoT demonstrated outstanding accuracies of 98.98% in binary scenarios and 99.92% in multiclass scenarios, with the IoTID20 datasets achieving notable accuracies of 99.94% in binary and 100% in multiclass scenarios, thus surpassing previous methodologies. The framework undergoes extensive experiments with two recent multiclass datasets, namely the Aalto and RT-IoT 2022 datasets, in which OASIS achieved 99.99% accuracy on the Aalto dataset and 96.52% on the RT-IoT 2022 dataset. Additionally, we compare our framework with various concept drift datasets and leading drift ensemble frameworks for performance comparison.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101545"},"PeriodicalIF":6.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549159","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":"Programming IoT systems: A focused conceptual framework and survey of approaches","authors":"Roberto Casadei ,&nbsp;Fabrizio Fornari ,&nbsp;Stefano Mariani ,&nbsp;Claudio Savaglio","doi":"10.1016/j.iot.2025.101548","DOIUrl":"10.1016/j.iot.2025.101548","url":null,"abstract":"<div><div>Any software engineer of Internet of Things (IoT) systems deals with three macro issues: how to perceive the properties of interest through sensors (<em>sensing</em> facet), how to process such information to decide what to do to achieve the system goals (<em>processing</em> facet), and how to enact such decisions by affecting the IoT system itself and its deployment environment accordingly (<em>actuation</em> facet). For each, one can either develop ad-hoc solutions from scratch, with mainstream programming languages, or build on top of existing IoT-specific software libraries, frameworks, and platforms. Here, we survey the broad state of the art of “IoT programming”, with a focus on clarifying which and how <em>programming paradigms and platforms</em> deal with four key features demanded by modern IoT systems: <em>scale-independence</em>, <em>situatedness</em>, <em>adaptiveness</em>, and <em>opportunistic deployment</em>, along the aforementioned three facets. We motivate such needs by describing compelling contemporary and near future scenarios. Then, we propose a reference <em>conceptual framework of programming IoT systems</em> with the goal of <em>(i)</em> uncovering which research areas are mostly active in IoT programming, and <em>(ii)</em> placing the state of the art at the intersection between the appropriate features and facets, to both <em>(iii)</em> clarify which approaches are most suited for different kinds of tasks, and <em>(iv)</em> emphasising open challenges. This conceptual framework is a novel contribution in the landscape of IoT programming surveys, and is intended to be a practical aid for researchers and practitioners that are deciding which computational tools (e.g. languages and platforms) to adopt while building their own IoT systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101548"},"PeriodicalIF":6.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511987","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":"A predictive maintenance architecture for TFT-LCD manufacturing using machine learning on the cloud service","authors":"Chih-Hung Chang ,&nbsp;Hsin-Ta Chiao ,&nbsp;Hsiang-Ching Chang ,&nbsp;Endah Kristiani ,&nbsp;Chao-Tung Yang","doi":"10.1016/j.iot.2025.101541","DOIUrl":"10.1016/j.iot.2025.101541","url":null,"abstract":"<div><div>The rise of Industry 4.0 has brought the world to intelligent manufacturing. The manufacturing industry combines technologies such as the Internet of Things, big data, and AI. Recent developments can further analyze equipment maintenance work by collecting real-time machine statuses, such as temperature and other parameter information. To achieve predictive machine maintenance, perform device maintenance and repair in advance to avoid unexpected downtime and affect production line operation. This paper will take the industry of TFT LCD panel component manufacturing as an experimental field and implement the predictive maintenance system of the TFT LCD machine through the Azure cloud service platform. First, the Pearson correlation was run to find a strong correlation for parameter training. In this case, Spark was used to reduce the processing time that initially took 2 h to 43 s and increase the speed by 99.4%. An optimization of the partition of the data table increased the operating cost, the IO cost, and the CPU cost by 98.77%, 98.78%, and 98.74%, respectively. Different training data and nodes are also compared to find excellent results. KNN, RF, XGBoost and SVM were compared to select a model that would be most suitable for use in the TFT LCD case. Finally, the results of the data and model analysis were visualized in real-time Azure Kubernetes scoring.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101541"},"PeriodicalIF":6.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511985","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":"Fingerprinting chipless RFID with a MIMO system for tag authentication in Internet of Things","authors":"Shahed Khan ,&nbsp;Biplob Ray ,&nbsp;Nemai Karmakar","doi":"10.1016/j.iot.2025.101542","DOIUrl":"10.1016/j.iot.2025.101542","url":null,"abstract":"<div><div>This paper presents a new method to tackle the security issues of chipless tag systems in Internet of Things (IoT) applications. The strategy aims to prevent the cloning of tags by utilizing the intrinsic natural randomness in the manufacturing process. This research presents a novel approach to generate fingerprints for chipless Radio Frequency Identification (RFID) tags using the unique backscattered electromagnetic (EM) responses, caused by inherent natural variations in resonator geometry, captured by a portable Multiple Input Multiple Output (MIMO) reader. Using twenty-two tags, one authentic and twenty-one counterfeits, two separate sets of fingerprints were generated using both Frequency Domain (FD) and Time Domain (TD) data, respectively. The clone detection model achieved an accuracy of 98.41% in a 35 dB noisy environment using fingerprints generated from FD data and 92.07% in a 50 dB noisy environment using fingerprints derived from TD data. This was obtained by utilizing similarity metrics such as Mean Squared Error (MSE) and Structural Similarity Index (SSI), offering a robust alternative to traditional tag authentication methods. The portability and affordability of the MIMO reader, combined with new opportunities for image-based identification, position this approach as a substantial advancement in the realm of authenticity verification for chipless RFID tags in the realm of IoT.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101542"},"PeriodicalIF":6.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488786","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":"Artificial intelligence and internet of things to improve smart hospitality services","authors":"Kuo Cheng Chung ,&nbsp;Paul Juinn Bing Tan","doi":"10.1016/j.iot.2025.101544","DOIUrl":"10.1016/j.iot.2025.101544","url":null,"abstract":"<div><div>Advances in artificial intelligence (AI) and the Internet of Things (IoT) have significantly reshaped the hospitality sector by introducing intelligent operations and tailored services. This research explores how the AIoT-enabled service robots influence hotel employees’ psychological and operational dynamics. Specifically, it examines the interplay among job demands, resources, cognitive trust, and perceived behavioral control within the context of job demands and resources theory. The study analyzes employees’ job-related factors and establishes a conceptual framework that highlights how these elements shape employees’ experiences with service robots. Data were analyzed using SPSS 21 and SmartPLS software. The analysis revealed that self-efficacy enhances cognitive trust and perceived behavioral control, thus boosting employees’ confidence in working alongside robots and streamlining operations. Conversely, threat appraisals were found to undermine these benefits by exacerbating feelings of job insecurity. Responsiveness and interactivity positively influenced cognitive trust and perceived behavioral control, while anthropomorphic traits influenced only the latter. Familiarity with technology further amplified these effects. The findings underscore the necessity of cognitive trust, confidence, and technology familiarity among employees, thus offering actionable insights for hoteliers to optimize human–machine collaboration, harmonize innovation with employee welfare, and achieve sustainable, intelligent development.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101544"},"PeriodicalIF":6.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526920","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 robust wireless body area network design","authors":"Mohammad Ali Raayatpanah ,&nbsp;Atefeh Abdolah Abyaneh ,&nbsp;Jocelyne Elias ,&nbsp;Fabio Martignon","doi":"10.1016/j.iot.2025.101540","DOIUrl":"10.1016/j.iot.2025.101540","url":null,"abstract":"<div><div>The Internet of Things (IoT) has reshaped technology paradigms through the integration of intelligent components like sensors, paving the way to the development of Wireless Body Area Networks (WBANs) specifically tailored for healthcare applications. However, designing an efficient WBAN requires addressing several challenges, including energy-efficient routing and data rate uncertainty. In response to these challenges, this paper proposes a novel approach — a two-stage robust programming formulation — for WBAN design. The primary aim is to minimize both energy consumption and relay placement costs, all while accounting for the inherent uncertainty in data rates. The proposed formulation explicitly addresses data rate uncertainties, leveraging robust optimization techniques to handle this uncertainty. We prove that efficiently solving an approximation of this robust formulation is achievable. Numerical results, measured in a set of realistic WBAN scenarios, demonstrate the effectiveness of the introduced two-stage robust programming formulation in achieving notable reductions in energy consumption and relay placement costs within the context of WBANs.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101540"},"PeriodicalIF":6.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480558","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 authenticated key exchange for low-power IoT networks using EDHOC","authors":"Alejandro Arias-Jimenez ,&nbsp;Jorge Gallego-Madrid ,&nbsp;Jesus Sanchez-Gomez ,&nbsp;Rafael Marin-Perez","doi":"10.1016/j.iot.2025.101539","DOIUrl":"10.1016/j.iot.2025.101539","url":null,"abstract":"<div><div>Energy efficiency is crucial for battery-powered devices in constrained networks, especially in Smart Agriculture and Smart Cities scenarios. To maximize battery life and ensure secure communications, lightweight key exchange protocols like Ephemeral Diffie–Hellman Over COSE (EDHOC) are essential. To further optimize energy efficiency, EDHOC can be combined with the Static Context Header Compression (SCHC) protocol, which is designed to compress and fragment data packets. This work demonstrates that EDHOC and SCHC can be successfully integrated to establish secure session keys in Internet of Things (IoT) scenarios. The attained results showcase that security mechanisms can be implemented in resource-limited devices with minimal energy impact, extending battery life. The experiments showed it is possible to compress the EDHOC exchange messages up to a <span><math><mo>∼</mo></math></span>54% and to reduce the energy consumption by a <span><math><mo>∼</mo></math></span>20%, while maintaining the CPU time levels in a cost-effective way. By designing IoT devices with these directives, it is possible to reduce the overall environmental footprint and increase the devices’ operational lifespan.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101539"},"PeriodicalIF":6.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454508","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":"FedWDP: A Wasserstein-distance-based federated learning for privacy and heterogeneous data in IoT","authors":"Jinlong Bai,&nbsp;Lifeng Cao,&nbsp;Jinhui Li,&nbsp;Jiling Wan,&nbsp;Xuehui Du","doi":"10.1016/j.iot.2025.101532","DOIUrl":"10.1016/j.iot.2025.101532","url":null,"abstract":"<div><div>As the need for interconnected devices and data exchange grows in the Internet of Things (IoT), traditional centralized data processing methods increasingly struggle to maintain privacy and adapt to the diverse and dispersed nature of IoT devices. Federated learning, a decentralized approach to machine learning, presents a viable solution to these challenges. Yet, the varied nature of IoT data and stringent privacy requirements introduce unique obstacles for federated learning. This paper introduces FedWDP, a federated learning method specifically designed for IoT privacy needs and heterogeneous data. FedWDP uses the Wasserstein distance to quantify the gap between local and global parameters, integrating this measure as a regularization term in the loss function to reduce model discrepancies and improve accuracy. To further balance privacy and usability, an exponential decay strategy is implemented, allowing for adaptive distribution of differential privacy noise. For better performance on high-dimensional data, PCA-FedWDP is proposed, which combines principal component analysis (PCA) with differentially private federated learning to perform dimensionality reduction. Experimental results on non-IID datasets reveal that this approach significantly enhances both accuracy and availability for heterogeneous data while safeguarding user privacy. This study thus provides a valuable framework for applying federated learning in IoT settings, contributing to the secure and intelligent use of IoT data in both theoretical and practical contexts.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101532"},"PeriodicalIF":6.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454509","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":"A crossover-integrated Marine Predator Algorithm for feature selection in intrusion detection systems within IoT environments","authors":"Sharif Naser Makhadmeh ,&nbsp;Salam Fraihat ,&nbsp;Mohammed Awad ,&nbsp;Yousef Sanjalawe ,&nbsp;Mohammed Azmi Al-Betar ,&nbsp;Mohammed A. Awadallah","doi":"10.1016/j.iot.2025.101536","DOIUrl":"10.1016/j.iot.2025.101536","url":null,"abstract":"<div><div>In recent times, there has been a significant rise in cyberattacks targeting the Internet of Things (IoT) and cyberspace in general. Detecting intrusions in a time series environment is a critical challenge for Network Intrusion Detection Systems (NIDS). Building an effective NIDS requires carefully establishing an efficient model, with machine learning (ML) playing a prominent role. The performance of ML models depends on selecting the most informative feature subset. Recently, metaheuristic (MH) optimization methods have been effective in identifying these key features. However, standard MH methods require adjustment to incorporate NIDS-specific knowledge for optimal results, improving both MH performance and ML accuracy. This paper introduces a novel NIDS framework based on three key phases: preprocessing, optimization, and generalization. In the preprocessing phase, several datasets undergo cleaning and under-sampling. In the optimization phase, an enhanced version of the Marine Predators Algorithm (MPA) is proposed, utilizing the crossover operator to identify the most relevant features. The proposed method is called MPAC. The crossover operator is utilized to boost the exploitation capabilities of the MPA and find the optimal local solution for the NIDS. Finally, the selected features are applied to the NIDS. Eight different datasets are employed for examination and evaluation using different evaluation measurements to assess the effectiveness of the proposed NIDS. The experimental evaluation is organized into three phases: evaluating the proposed crossover modification by applying it to five algorithms and comparing results to the originals, comparing the results of the proposed algorithms to prove the robust performance of the MPAC, and comparing the results obtained by the MPAC with the stat-of-the-arts. The proposed MPAC confirmed its demonstration and high performance in detecting network attacks, wherein in the first evaluation phase, the proposed approach obtained better results in almost 90% of the comparisons. In the second comparison phase, the proposed MPAC achieved better results in six datasets out of eight, and in the last phase, the MPAC outperforms all compared methods.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101536"},"PeriodicalIF":6.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437249","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":"Bridging FANETs and MANETs for synchronous data collection in precision agriculture activities using AirPro-FL: An energy aware fuzzy logic routing protocol","authors":"Georgios Kakamoukas ,&nbsp;Anastasios Economides ,&nbsp;Stamatia Bibi ,&nbsp;Panagiotis Sarigiannidis","doi":"10.1016/j.iot.2025.101535","DOIUrl":"10.1016/j.iot.2025.101535","url":null,"abstract":"<div><div>The use of Flying Ad-hoc Networks (FANETs) in precision agriculture requires the development of advanced routing protocols to manage UAV-specific challenges effectively. This paper presents AirPro-FL, a proactive routing protocol that uses fuzzy logic to optimize UAV performance in precision agriculture tasks. Unlike conventional FANET research, which often relies on stochastic mobility models that do not accurately reflect real-world agricultural missions, AirPro-FL is designed to address these gaps by enhancing UAV cooperation in scanning operations such as crop scouting, crop surveying and mapping, spraying applications, and geofencing. Traditionally, these agricultural activities rely on a single UAV, often resulting in inefficiencies. The UAV’s limited real-time data transmission capabilities, vulnerability to operational failures, and potential mission execution delays contribute to reduced overall effectiveness. The proposed system involving multiple UAVs significantly speeds up mission completion and enables real-time data transfer through the cooperation between FANETs and Mobile Ad-hoc Networks (MANETs). This innovation empowers agricultural stakeholders to make faster and more reliable decisions based on accurate data collection. Simulation results indicate that AirPro-FL consistently achieves the highest Packet Delivery Ratio (PDR) across all scenarios, halves the average end-to-end delay compared to the second-best protocol, and exhibits superior energy efficiency. The protocol’s success in optimizing data collection during scanning operations underscores its broader applicability beyond agriculture, extending to other fields such as environmental monitoring, disaster management, and surveillance, where similar mobility patterns are employed.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101535"},"PeriodicalIF":6.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430074","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}