Internet of Things最新文献

{"title":"Smart-contract-based blockchain-enabled decentralized scheme for improving smart-grid security","authors":"Bhabendu Kumar Mohanta ,&nbsp;Ali Ismail Awad ,&nbsp;Tarek Elsaka ,&nbsp;Hamza Kheddar ,&nbsp;Ezedin Baraka","doi":"10.1016/j.iot.2025.101811","DOIUrl":"10.1016/j.iot.2025.101811","url":null,"abstract":"<div><div>Intelligent devices with embedded technology have proliferated dramatically over the past decade. The Internet of Things (IoT) has emerged as a transformational force, advancing traditional systems to previously unattainable levels of intelligence. Smart cities, transportation, healthcare, supply-chain management, agriculture, water management, and smart grid (SG) systems are among the industries where the IoT has found applications. These developments are demonstrated by the integration of IoT systems into SG networks, offering significant improvements in sustainability, dependability, and efficiency. Such systems use various IoT devices to continuously monitor the environment and transmit data for processing and analysis. Nonetheless, the growth of the IoT has introduced security vulnerabilities, including concerns about user identification, data integrity, and trust, especially in SG applications. This study aims to resolve several security challenges in IoT-enabled SG applications to support sustainability. The proposed scheme effectively tackles critical security requirements such as data integrity, user anonymity, distributed storage, trust management, and decentralized architecture. The security concerns addressed by blockchain technology include preserving data integrity, fostering trust, providing secure communication, and enabling effective monitoring. Smart contracts automate system processes and are effective in maintaining user trust. The experimental findings support the viability of the proposed system, demonstrating a computational cost of 3.150 ms and a communication overhead of 992 bits, both representing improvements over various existing solutions. Additionally, the deployment cost for the smart contract is found to be 5.64 USD with a writing cost of 2.89 USD, both of which are lower than the costs associated with comparable approaches.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101811"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415712","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":"Resource-efficient fog computing vision system for occupancy monitoring: A real-world deployment in university libraries","authors":"Alejandro S. Martínez-Sala,&nbsp;Lucio Hernando-Cánovas,&nbsp;Juan C. Sánchez-Aarnoutse,&nbsp;Juan J. Alcaraz","doi":"10.1016/j.iot.2025.101748","DOIUrl":"10.1016/j.iot.2025.101748","url":null,"abstract":"<div><div>This paper presents a fog computing system for real-time occupancy monitoring across three university libraries, using ceiling-mounted, top-view cameras positioned above each entrance. Video streams from low-cost cameras are securely transmitted to a fog server deployed within the university’s intranet. Top-view person tracking ensures privacy compliance by inherently eliminating facial recognition, but introduces challenges such as non-standard human appearance, occlusions, and lighting variations. For person detection, we employ a YOLOv5 model initially trained on top-view human annotations, further refined through transfer learning using a curated dataset from the three libraries. The system features a two-stage processing pipeline. First, a lightweight background subtraction algorithm filters frames with potential motion, which are queued via RabbitMQ for sequential processing. Second, a People Flow Counting module applies the optimized YOLOv5 model to detect and count individuals in each frame, followed by a custom tracking algorithm and virtual line-crossing logic to ensure accurate flow tracking. Each library is handled independently through a batch processing approach, updating occupancy estimates with bounded delay using a single CPU-only fog server. This architecture maintains low latency while avoiding server overload and minimizing energy use. The system has been in continuous production for over twelve months, demonstrating reliable performance across all three libraries on commodity hardware. Quantitative evaluation confirms 94 % accuracy in people flow detection, validating the system’s robustness, scalability, and practical utility for long-term, privacy-preserving deployment in smart campus environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101748"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989076","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":"2025-11-01","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":"2025-11-01","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":"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":"2025-11-01","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":"Joint optimisation of time and energy consumption for data aggregation in fog-enabled IoT networks","authors":"Sira Yongchareon","doi":"10.1016/j.iot.2025.101775","DOIUrl":"10.1016/j.iot.2025.101775","url":null,"abstract":"<div><div>Fog computing extends cloud capabilities to the network edge, enabling Internet-of-Things (IoT) devices to offload computation to nearby fog nodes rather than a remote cloud. Offloading aggregation tasks reduces data redundancy and accelerates analytics while easing device energy use and backhaul load. Yet end-to-end completion time—comprising execution, transmission, and queueing—can still be substantial, creating a challenging time-energy trade-off. We formulate data-aggregation offloading as a multi-objective optimization problem that jointly minimizes latency (makespan) and energy under compute and bandwidth constraints. To solve it, we develop an NSGA-III-based method that searches for Pareto-optimal offloading and scheduling decisions across sensor and fog nodes. Comprehensive simulations and systematic experiments demonstrate that our approach consistently outperforms state-of-the-art baselines, delivering lower latency and energy consumption with better scalability.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101775"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266674","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 healthcare in the 6G and AI era: Opportunities and challenges","authors":"Houssein Taleb ,&nbsp;Guillaume Andrieux ,&nbsp;Daniele Khalife ,&nbsp;Alain Ajami ,&nbsp;Abbass Nasser","doi":"10.1016/j.iot.2025.101744","DOIUrl":"10.1016/j.iot.2025.101744","url":null,"abstract":"<div><div>The integration of AI with emerging 6G wireless communications promises to revolutionize healthcare delivery by providing ultra-fast, reliable, and intelligent medical services. Unlike previous generations of mobile phones, 6G is expected to offer sub-millisecond latency, data rates up to terabits per second, and connectivity for more than 10 million devices per square kilometer. Together, these technologies will enable unprecedented healthcare applications, such as real-time remote robotic surgery, holographic telemedicine, and continuous monitoring using bio-nanosensors within the bio-nano-internet of things.</div><div>This survey systematically analyzes the integration of AI and 6G technologies, focusing on how their convergence will enable enhanced edge computing, federated and generative AI models, low-latency analytics for personalized treatment, predictive diagnostics, and efficient resource utilization. We present a comprehensive comparison of 5G and 6G architectures, highlighting the limitations of current systems and demonstrating how 6G advancements can address critical healthcare needs, including data throughput, mobility, and security.</div><div>Furthermore, this work identifies detailed opportunities, such as AI-powered virtual nurse assistants, AI-enhanced drug discovery accelerated by hyper-responsive 6G infrastructures, and digital twin-enabled patient simulation. Alongside these opportunities, we critically examine the technical challenges related to spectrum management in the terahertz band, the design of energy-efficient IoT devices, robust data privacy frameworks that integrate federated learning and blockchain technology, ethical considerations surrounding AI explainability, and equitable access to healthcare.</div><div>By filling gaps in the existing literature, this paper presents a comprehensive framework that combines AI and 6G, specifically designed for healthcare systems. Our findings underscore the transformative potential of this combination for achieving proactive and accessible healthcare, while outlining a roadmap for overcoming prevailing technical, ethical, and infrastructural barriers.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101744"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933364","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":"TrustAIoT: A framework for building trustworthy AIoT platforms","authors":"Carlos Mario Braga ,&nbsp;Ángel Suarez-Bárcena ,&nbsp;Manuel A. Serrano ,&nbsp;Eduardo Fernández-Medina","doi":"10.1016/j.iot.2025.101751","DOIUrl":"10.1016/j.iot.2025.101751","url":null,"abstract":"<div><div>In an increasingly connected world, each device is expected to be linked to the internet and, consequently, to other objects with which it can communicate. As the Internet of Things (IoT) expands, so do social, legal, and ethical concerns. Moreover, IoT systems are evolving to process data and provide recommendations based on their findings, a capability that stems from the integration of Artificial Intelligence (AI) and Machine Learning technologies. The convergence of IoT and AI (AIoT), along with the unique aspects of AIoT architectures that differ from non-IoT-enabled AI systems, necessitates a thorough review of specific considerations for building trustworthy AIoT systems. Ensuring trustworthiness in AIoT is crucial due to the increased complexity and potential vulnerabilities introduced by this convergence.</div><div>This article introduces TrustAIoT, a structured framework for the development and long-term governance of trustworthy AIoT platforms. The framework integrates ethical, legal, and technical dimensions, and consists of both a multi-layer guideline and a lifecycle-oriented process tailored to the specific architectural characteristics of AIoT systems.</div><div>Based on a systematic literature review, trustworthiness-related technical, ethical, and legal elements are cross-referenced and contrasted with the operational and architectural needs of AIoT environments, ensuring that all critical aspects are addressed. Challenges in applying these elements across heterogeneous architectures are analyzed, leading to the definition of a guideline for transferring trust principles across the different layers of an AIoT platform, and a process for constructing and maintaining such platforms. A platform is understood as an environment comprising infrastructure, tools, services, processes, and components for developing, deploying, and operating applications.</div><div>The TrustAIoT framework consolidates these artifacts into a cohesive approach that supports trust-oriented decision-making throughout the platform lifecycle, from architectural design to project deployment.</div><div>The proposed guideline and process form a unified framework that ensures high trustworthiness standards, thereby enabling the reliable development of multiple projects and applications within AIoT ecosystems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101751"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019978","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":"Data security for cloud-fog-assisted Industrial Internet of Things (IIoT) in future Industry 5.0","authors":"Haowen Tan ,&nbsp;Max Hashem Eiza ,&nbsp;Sangman Moh ,&nbsp;Kouichi Sakurai","doi":"10.1016/j.iot.2025.101765","DOIUrl":"10.1016/j.iot.2025.101765","url":null,"abstract":"<div><div>Cloud–Fog assisted Industrial Internet of Things (IIoT) has emerged as a core enabling technology for Industry 5.0, driving innovations in smart manufacturing by facilitating real-time interactions among industrial devices, fog nodes, and cloud platforms. However, inherent limitations in computational power and adaptability of IIoT terminals pose significant challenges to data security protection. This special issue focuses on addressing critical data security issues in Cloud–Fog IIoT systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101765"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680989","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 systematic literature review on AI in IoT systems: Tasks, applications, and deployment","authors":"Umair Khadam ,&nbsp;Paul Davidsson ,&nbsp;Romina Spalazzese","doi":"10.1016/j.iot.2025.101779","DOIUrl":"10.1016/j.iot.2025.101779","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) into Internet of Things (IoT) systems has garnered considerable attention for its ability to enhance efficiency, functionality, and decision making. To drive further research and practical applications, it is essential to gain a deeper understanding of the different roles of AI in IoT systems. In this systematic literature review, we analyze 103 articles describing Artificial Intelligence of Things (AIoT) systems found in three databases, i.e. Scopus, IEEE Xplore, and Web of Science. For each article, we examined the tasks for which AI was used, the input and output data, the application domain, the maturity level of the system, the AI methods used, and where the AI components were deployed. As a result, we identified six general tasks of AI in IoT systems, and thirteen subtasks, the most frequent being prediction, object and event recognition, and operational decision-making. Moreover, we conclude that most AI components in IoT systems process numeric data as input and that healthcare is the most common application domain followed by farming and transportation. Our analysis further revealed that most AIoT systems are in early development stages not validated in real environments. We also identified that Convolutional Neural Networks is the most frequently employed AI method, with supervised learning being the dominant approach. Additionally, we found that both AI deployment, either in the cloud or at the edge, are frequent, but that hybrid deployment is not that common. Finally, we identified key gaps in current AIoT research and based on this, we suggest directions for future research.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101779"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220821","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}