Internet of Things最新文献

{"title":"Big data and Internet of Things applications in smart cities: Recent advances, challenges, and critical issues","authors":"Elias Dritsas,&nbsp;Maria Trigka","doi":"10.1016/j.iot.2025.101770","DOIUrl":"10.1016/j.iot.2025.101770","url":null,"abstract":"<div><div>The rapid urbanization of modern cities has been propelled by the convergence of the Internet of Things (IoT) and Big Data, enabling real-time monitoring, predictive analytics, and intelligent automation across transportation, energy, healthcare, and public safety. This survey systematically reviews advancements in IoT-enabled infrastructure, Big Data analytics, edge and cloud integration, digital twin technology, and blockchain for secure and scalable data management. The literature selection focused on peer-reviewed works published from 2020 onward, prioritizing journal and conference papers that present concrete smart city deployment or technical frameworks. Each technological domain is examined with respect to its operational benefits, limitations, and role in enabling resilient urban ecosystems. Emerging trends such as 6G-enabled IoT, federated learning (FL), quantum computing, and swarm intelligence are explicitly contextualized in terms of their maturity, current research focus, and prospective impacts. To address persisting challenges, including scalability bottlenecks, interoperability gaps, cybersecurity threats, and data governance issues, the survey identifies actionable directions such as geospatial data standardization, energy-aware orchestration, and lightweight consensus mechanisms. These targeted measures provide a foundation for guiding future research and practice toward secure, efficient, and human-centric smart city development.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101770"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220822","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":"GUIDE2FR: A smart monitoring platform with a digital twin of a firefighter training tower for emergency scenarios","authors":"Marcos Delgado Álvaro ,&nbsp;Robert Novak ,&nbsp;Pedro Rafael Fernández Barbosa ,&nbsp;Iván Chicano Capelo ,&nbsp;Micael Gallego ,&nbsp;M. Cristina Rodriguez-Sanchez","doi":"10.1016/j.iot.2025.101768","DOIUrl":"10.1016/j.iot.2025.101768","url":null,"abstract":"<div><div>This paper describes the implementation of a digital twin for buildings to enhance the emergency response capabilities of first responder teams, including firefighters, police, and emergency medical services. The proposed platform improves the planning of preventive evacuation strategies and supports real-time operational decisions during emergencies. It integrates wireless monitoring beacons, specifically designed for hostile environments, a cloud-based data management system, and a predictive model to monitor environmental air quality parameters, which are critical during emergency scenarios. The platform uses a digital twin to simulate the building’s behavior, incorporating multimedia content and time series graphs to enhance situational awareness and decision-making. Real-time building data are seamlessly integrated with predictive models generated from smart building sensors, offering a comprehensive visualization on a web-based monitoring interface. This approach provides critical insights to decision-makers, improving the safety and efficiency of both rescue operations and preventive measures.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101768"},"PeriodicalIF":7.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158238","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":"Robust priority aware multi-criterion offloading in digital twin UAVs networks","authors":"Muhammad Yahya ,&nbsp;Muhammad Naeem ,&nbsp;Zeeshan Kaleem ,&nbsp;Waleed Ejaz","doi":"10.1016/j.iot.2025.101763","DOIUrl":"10.1016/j.iot.2025.101763","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) play a critical role in replenishing the energy of power-constrained Internet of Things (IoT) devices, particularly in public safety operations, thereby maintaining continuous system functionality. Integrating Mobile Edge Computing (MEC) into UAV platforms enables offloading computational tasks to aerial nodes, optimizing resource utilization. Efficient orchestration of communication, computation, caching, and energy resources is imperative to maximize the benefits of UAV-assisted MEC networks. Additionally, ensuring high situational awareness is essential for supporting priority-based latency-sensitive applications. Digital twin technology can be instrumental in minimizing latency by generating a real-time digital representation of the physical infrastructure, enabling enhanced system monitoring and optimization. Accordingly, we have formulated an optimization problem to maximize the number of IoT devices UAVs can support while adhering to predefined constraints. The formulated problem is a mixed integer non-linear programming model. Additionally, the dynamic management of tasks with varying priorities and computational demands introduces a significant resource allocation and scheduling challenge. Our proposed approach entails an efficient task offloading and priority-based scheduling strategy that prioritizes tasks, allocating computational resources to those with higher priority. The approach encompasses a multi-stage offloading strategy combining an interior-point method with a learning algorithm to address the inherent complexity and provide a viable solution. Simulation results validate the effectiveness of the proposed approach, outperforming conventional methods. Specifically, the Penalty Function Method Heuristic combined with the Interior Point Method achieves superior user connectivity compared to the Simple Relaxation Heuristic strategy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101763"},"PeriodicalIF":7.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158236","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":"CARES: A Hybrid caregivers recommendation system using deep learning and knowledge graphs","authors":"Qiaoyun Zhang ,&nbsp;Sze-Han Wang ,&nbsp;Chung-Chih Lin ,&nbsp;Chih-Yung Chang ,&nbsp;Diptendu Sinha Roy","doi":"10.1016/j.iot.2025.101769","DOIUrl":"10.1016/j.iot.2025.101769","url":null,"abstract":"<div><div>Recommendation systems have prospered by leveraging user-item interactions and their features for personalized recommendations. Recent advancements in deep learning further enhance these recommendation systems with powerful backbones for learning from user-item data. However, solely depending on these interactions often leads to the cold-start problem, where items lacking historical data cannot be effectively recommended. Additionally, the issue of high similarity between user and item features frequently goes unresolved. This paper introduces a Hybrid Caregiver Recommendation mechanism, called CARES, designed to recommend suitable caregivers for postpartum women using deep learning and knowledge graphs. Initially, the proposed CARES utilizes Extreme Gradient Boosting (XGBoost) to identify important features, addressing the issue of feature similarity. Then it employs <em>K</em>-Means clustering to group postpartum women and caregivers based on similar features. Subsequently, it utilizes a Deep &amp; Cross Network (DCN) to automatically learn feature interactions and constructs knowledge graphs to tackle the cold start problem. The proposed CARES also integrates exploration and exploitation strategies to balance the accuracy and diversity of recommendations. The proposed CARES compares with existing mechanisms on real datasets, and the simulation results demonstrate its effectiveness in terms of precision, recall, and F1-Score.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101769"},"PeriodicalIF":7.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158240","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 big data analysis in IoT applications and optimizing the performance of machine learning models using hybrid dimensionality optimization approach","authors":"Ihab Nassra,&nbsp;Juan V. Capella","doi":"10.1016/j.iot.2025.101764","DOIUrl":"10.1016/j.iot.2025.101764","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The proliferation of Internet of Things (IoT) applications generates high-dimensional datasets characterized by substantial velocity, variety, and complexity, imposing severe computational constraints on machine learning systems. Such data's high dimensionality complicates identifying meaningful correlations among features. Thus, high-dimensional datasets pose substantial challenges for machine learning, as the abundance of variables tends to obscure meaningful correlations and hinder practical data analysis, particularly regarding computational resource consumption (e.g., memory usage), processing time, and machine learning models' training efficiency and performance. Dimensionality reduction techniques address these challenges by decreasing the number of input variables and preserving the intrinsic structure of the data while alleviating computational burdens. Nevertheless, most contemporary methods are optimized for either linear or nonlinear data patterns, but rarely both. Hybrid strategies integrating linear and nonlinear reduction techniques have increasingly addressed these constraints. Specifically, the combination of Principal Component Analysis (PCA) as a preprocessing stage with Restricted Boltzmann Machines (RBMs) offers a complementary solution, wherein PCA condenses the feature space into a lower-dimensional representation, thereby improving training efficiency and enabling RBMs to capture complex nonlinear dependencies with enhanced convergence and generalization. While this combination can theoretically exploit the data's linear and nonlinear characteristics, conventional PCA-RBM frameworks often struggle to retain essential local manifold structures, limiting their effectiveness in capturing the full complexity of real-world datasets. This study addresses these challenges by proposing a novel hybrid dimensionality reduction framework that integrates PCA's global linear projection capabilities with RBMs' nonlinear feature learning strengths through an adaptive graph regularization mechanism that preserves critical local manifold properties, which address the limitations of conventional PCA-RBM combinations. The adaptive regularization mechanism ensures that proximate data points in input space retain similarity in the reduced feature space, effectively bridging global and local structure preservation. Compared to conventional methods, experimental validation demonstrates superior performance across multiple evaluation metrics, including data reduction efficiency, classification accuracy, precision, recall, and F-score. The framework addresses critical limitations in high-dimensional data processing while maintaining model performance, establishing a methodologically significant contribution to dimensionality reduction techniques applicable across scientific disciplines handling complex IoT-generated datasets. Our findings indicate that dimensionality reduction constitutes a viable and efficacious approach to simplifying ","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101764"},"PeriodicalIF":7.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158239","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":"Role of artificial intelligence in health monitoring using IoT based wearable sensors: A survey","authors":"Laxmi Shaw,&nbsp;Hardik A. Gohel","doi":"10.1016/j.iot.2025.101761","DOIUrl":"10.1016/j.iot.2025.101761","url":null,"abstract":"<div><div>With the rising demand for rapid and accurate medical diagnosis and the widespread adoption of Internet of Things (IoT) technologies, healthcare delivery is undergoing a major transformation in patient monitoring, diagnosis, and prognosis. The growth of virtual healthcare providers and remote consultations further increases the need for efficient systems that can process large volumes of real-time health data. In this context, integrating artificial intelligence (AI) with IoT-based healthcare systems plays an essential role in enabling predictive analytics, early anomaly detection, and real-time clinical decision-making. This paper examines the role of AI in enhancing the performance of IoT-enabled wearable health monitoring devices, with a focus on data transmission efficiency, energy consumption, communication protocols, and overall system reliability. Our analysis highlights that AI integration significantly improves accuracy, adaptability, and patient-centric outcomes. The paper concludes by outlining the current challenges such as energy limitations, data privacy, and interoperability and discusses future research directions for developing next-generation IoT-based wearable healthcare systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101761"},"PeriodicalIF":7.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096289","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":"DCATCN: A temporal convolutional network and cross-attention based UAV sensor anomaly detection method","authors":"Bo Wu ,&nbsp;Chengzi Zhou ,&nbsp;Yanxi Liu ,&nbsp;Peng Xiao ,&nbsp;Wei Zheng","doi":"10.1016/j.iot.2025.101760","DOIUrl":"10.1016/j.iot.2025.101760","url":null,"abstract":"<div><div>To address the issues of severe high-dimensional redundancy and interference in UAV sensor data, insufficient ability to model temporal dependencies, and anomaly decisions relying on fixed empirical thresholds, this paper proposes a lightweight anomaly detection framework called DCATCN (Dual Cross-Attention Temporal Convolutional Network) based on a bidirectional temporal convolutional network (BiTCN) and a Cross-Attention mechanism. First, the method uses the Maximal Information Coefficient (MIC) to adaptively select a feature subset that is highly correlated with target anomalies, effectively reducing data redundancy; then it constructs a bidirectional temporal convolutional network to extract forward and backward features of the time series data in parallel, introducing a Cross-Attention mechanism to dynamically integrate bidirectional information and enhance the model’s representation of temporal dependencies; finally, it employs Extreme Value Theory to statistically model the prediction residuals and determine the anomaly decision threshold, achieving robust and reliable anomaly detection. Comprehensive experiments on the public ThorFlight93 dataset demonstrate that this method outperforms various mainstream models in both detection accuracy and computational efficiency, showcasing strong potential for engineering applications. Code release: <span><span>https://github.com/ZCchou/DCATCN.git</span><svg><path></path></svg></span></div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101760"},"PeriodicalIF":7.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158446","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":"Agri-farming with computer vision, IoT and blockchain towards climate smart cultivation","authors":"Sajid Safeer ,&nbsp;Pierluigi Gallo ,&nbsp;Cataldo Pulvento","doi":"10.1016/j.iot.2025.101749","DOIUrl":"10.1016/j.iot.2025.101749","url":null,"abstract":"<div><div>Modern agriculture faces critical challenges such as climate change, food security and supply chain inefficiencies, which demand innovative solutions. Traditional farming systems often lack real time monitoring, data security and transparency, leading to wastefulness and quality concerns. To address these, we present a comprehensive precision agriculture framework that integrates Internet of Things (IoT) sensors, Raspberry Pi (R-Pi) edge computing, blockchain based data management and computer vision (CV) assisted statistical modeling. The system collects environmental data via a sensor network, processes it at the edge using R-Pi, and records summarized outputs on a secure Ethereum based blockchain using smart contracts. Simultaneously, CV modules perform real time quality assessment and anomaly detection. A Markov chain based stochastic model is employed to track quality degradation in high value crops. The methodology is validated through a saffron use case, demonstrating effectiveness in monitoring filament degradation and detecting potential fraud. This integration enhances real time decision making, ensures traceability and promotes sustainability in climate smart agriculture.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101749"},"PeriodicalIF":7.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096287","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 dynamic anonymous authentication protocol with membership privacy in cloud-fog-assisted IIoT","authors":"Guojun Wang ,&nbsp;Guixin Jiang ,&nbsp;Yushuai Zhao","doi":"10.1016/j.iot.2025.101742","DOIUrl":"10.1016/j.iot.2025.101742","url":null,"abstract":"<div><div>The cloud-fog computing infrastructure flexibly deploys resources in the Industrial Internet of Things (IIoT), adjusting the computing and storage capabilities of cloud and fog nodes based on specific needs to optimize costs and performance. However, the frequent transmission and sharing of data between terminal entities and the cloud-fog infrastructure can easily lead to privacy leaks of terminals or fog nodes. Positioned at the edge of the data source, fog nodes manage and process the needs of various local industrial sensors in real time. When sensors dynamically join or leave the group, frequent authentication with fog nodes can compromise identity privacy. To protect node identity privacy and reduce computational costs, the group signature technology is introduced in this paper. It is usually employed to build anonymous authentication protocols because of its natural properties. Backes et al. proposed a novel practical property called membership privacy for dynamic group signatures, which provides stronger anonymity. In this paper, we employ the BBS+ signature, signature proof of knowledge (SPK), ElGamal scheme, etc., to design a novel verifier local revocation (VLR) dynamic group signature with membership privacy. The proposed group signature has a smaller signature size and less computational overhead. Afterward, the framework of the cloud-fog-assisted IIoT scheme based on the proposed group signature is constructed, capturing full anonymity to preserve the privacy of patients. Formal security proofs are presented to show that the proposed group signature satisfies both general and specific security requirements. Finally, the overhead of the group signature scheme is tested on type d159 curves in the Java pairing-based cryptography (JPBC) library. The results demonstrate that our scheme is more suitable for resource-restrained devices.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101742"},"PeriodicalIF":7.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158237","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 comprehensive review of IoT device fingerprinting: Insights into techniques, trends, challenges, and future directions","authors":"Mariam Munsif Mir,&nbsp;Wee Lum Tan,&nbsp;Mohammad Awrangjeb","doi":"10.1016/j.iot.2025.101758","DOIUrl":"10.1016/j.iot.2025.101758","url":null,"abstract":"<div><div>The Internet of Things (IoT) connects billions of devices, ranging from household appliances to industrial systems, enabling intelligent automation, real-time monitoring, and seamless communication. However, the rapid expansion in the IoT ecosystem introduces significant security and management challenges, particularly in device identification and authentication. IoT device fingerprinting has emerged as a critical research area for enhancing security and management in interconnected ecosystems.</div><div>The article at hand provides a comprehensive review and analysis of existing IoT device fingerprinting methods from 2017 to 2025. It categorizes these methods based on their underlying approaches across the Physical, Network, and Application communication layers. Each study is critically examined, with a focus on its characteristics, strengths, and limitations. The article also reviews publicly available datasets and explores trends in feature selection, including the use of statistical, radio frequency, and network packet features. Moreover, it also examines the adoption of machine learning and deep learning models in this context. Finally, the article addresses existing challenges, use cases, and outlines future research directions to support the development of more effective and scalable solutions in this domain.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101758"},"PeriodicalIF":7.6,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118704","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}