Internet of Things最新文献_第5页

Reformed multi-source sharing (RMSS) for efficient resource distribution across edge servers 改进了多源共享（RMSS），以实现跨边缘服务器的有效资源分配

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-08 DOI: 10.1016/j.iot.2025.101848

Kannan Srinivasan , Guman Singh Chauhan , Rahul Jadon , Rajababu Budda , Venkata Surya Teja Gollapalli , Joseph Bamidele Awotunde

{"title":"Reformed multi-source sharing (RMSS) for efficient resource distribution across edge servers","authors":"Kannan Srinivasan , Guman Singh Chauhan , Rahul Jadon , Rajababu Budda , Venkata Surya Teja Gollapalli , Joseph Bamidele Awotunde","doi":"10.1016/j.iot.2025.101848","DOIUrl":"10.1016/j.iot.2025.101848","url":null,"abstract":"<div><div>Mobile Edge Computing plays a critical role in enabling real-time services across heterogeneous applications by distributing computational resources across multiple servers. However, managing service efficacy in multi-resource environments remains a challenge, particularly concerning scalability and service deficiencies. This study aims to introduce a novel method, Reformed Multi-Source Sharing (RMSS), to enhance resource distribution and improve service efficiency in MEC environments. The propose RMSS, which periodically updates resource allocations based on service density and source sharing to optimize the sharing rate. The method employs federated learning to validate previous resource allocations, ensuring optimal service distribution and minimizing deficiencies. The system was evaluated using multi-server environments and edge devices. The proposed RMSS method effectively mitigates resource allocation deficiencies, leading to significant improvements in service response times and user support. RMSS demonstrated up to 9.77 % higher service response rates, 13.88 % lower latency, and reduced service deficiency by 11.89 %, compared to existing approaches. RMSS improves scalability and resource distribution in MEC, edge devices in dense user environments. Future research will focus on incorporating virtualization-based edge slicing to further reduce latency and optimize resource distribution in increasingly complex edge networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101848"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738839","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}

引用次数: 0

A domain-specific language and architecture for detecting process activities from sensor streams in IoT 一种领域特定的语言和架构，用于检测物联网中传感器流中的流程活动

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.iot.2025.101870

Ronny Seiger, Daniel Locher, Marco Kaufmann, Aaron F. Kurz

{"title":"A domain-specific language and architecture for detecting process activities from sensor streams in IoT","authors":"Ronny Seiger, Daniel Locher, Marco Kaufmann, Aaron F. Kurz","doi":"10.1016/j.iot.2025.101870","DOIUrl":"10.1016/j.iot.2025.101870","url":null,"abstract":"<div><div>Modern Internet of Things (IoT) systems are equipped with a large quantity of sensors providing real-time data about the current operations of their components, which is crucial for the systems’ internal control systems and processes. However, these data are often too fine-grained to derive useful insights into the execution of the larger processes an IoT system might be part of. Process mining has developed advanced approaches for the analysis of business processes that may also be used in the context of IoT. Bringing process mining to IoT requires an event abstraction step to lift the low-level sensor data to the business process level. In this work, we aim to enable domain experts to perform this step using a newly developed domain-specific language (DSL) called Radiant. Radiant supports the specification of patterns within the sensor data that indicate the execution of higher level process activities. These patterns are translated to complex event processing (CEP) applications to be used for detecting activity executions at runtime. We propose a corresponding software architecture that enables online event abstraction from IoT sensor streams using the CEP applications. We evaluate these applications to monitor activity executions in smart manufacturing and smart healthcare. These evaluations are useful to inform the domain expert about the quality of activity detections based on the specified patterns and potential for improvement via additional or modified patterns and sensors.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101870"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926731","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}

引用次数: 0

BPSmart-CARE: a framework for managing contextualized actions in IoT systems through the integration of business process modelling and complex event processing BPSmart-CARE：通过集成业务流程建模和复杂事件处理来管理物联网系统中情境化操作的框架

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1016/j.iot.2026.101887

Adrian Bazan-Muñoz , Cesare Pautasso , Guadalupe Ortiz , Alfonso Garcia-de-Prado

{"title":"BPSmart-CARE: a framework for managing contextualized actions in IoT systems through the integration of business process modelling and complex event processing","authors":"Adrian Bazan-Muñoz , Cesare Pautasso , Guadalupe Ortiz , Alfonso Garcia-de-Prado","doi":"10.1016/j.iot.2026.101887","DOIUrl":"10.1016/j.iot.2026.101887","url":null,"abstract":"<div><div>The exponential growth of Internet of Things (IoT) sensors and data has led to multiple software applications which aim at collecting, monitoring, controlling, and automating decisions based on such data in several domains such as smart cities. Even though early IoT frameworks and software architectures relied on correlating data from a single application domain, missing opportunities to improve contextualisation; in the last years several Complex Event Processing (CEP)-based frameworks and applications have been provided to improve the correlation of data from heterogenous domains, facilitating context-aware decision making. However, several issues remain unsolved: on the one hand, frameworks should not only provide the means for data correlation and contextualisation, but also for the definition of customisable real-time actions based on the context. On the other hand, CEP patterns update remains static in these frameworks, while there is a need for dynamically updating CEP pattern logic to adapt to changing contexts in real time. Even more, CEP patterns logic understanding might also be a handicap for developers. To face these challenges, in this paper, we enhance an existent CEP-based framework and software architecture by incorporating Business Process Modelling (BPM) to facilitate the definition of CEP patterns rationale, manage interdependencies between patterns, define and handle contextualised user actions, and enable real-time updating of CEP pattern logic based on detected situations. The proposed approach is illustrated through a case study, assessed via usability and performance evaluations, which show improvements in comprehensibility, maintainability, and updatability of CEP-based software applications for context-aware IoT scenarios.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101887"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173163","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}

引用次数: 0

Creation of AI-driven smart spaces for enhanced indoor environments – A survey 为增强室内环境创造人工智能驱动的智能空间——一项调查

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-18 DOI: 10.1016/j.iot.2026.101876

Aygün Varol , Naser Hossein Motlagh , Mirka Leino , Sasu Tarkoma , Johanna Virkki

{"title":"Creation of AI-driven smart spaces for enhanced indoor environments – A survey","authors":"Aygün Varol , Naser Hossein Motlagh , Mirka Leino , Sasu Tarkoma , Johanna Virkki","doi":"10.1016/j.iot.2026.101876","DOIUrl":"10.1016/j.iot.2026.101876","url":null,"abstract":"<div><div>Smart spaces are ubiquitous computing environments that integrate diverse sensing and communication technologies to enhance functionality, optimize energy utilization, and improve user comfort and well-being. The adoption of emerging artificial intelligence (AI) methodologies has led to the development of AI-driven smart spaces, further expanding capabilities through applications such as personalized comfort settings, interactive living spaces, and automation of space systems. These advancements collectively elevate the quality of indoor experiences for users. To systematically examine these developments, we present a comprehensive survey of the foundational components of AI-driven smart spaces, including sensor technologies, data communication protocols, network management and maintenance strategies, and data collection, processing, and analytics. We investigate both traditional machine learning (ML) methods, such as deep learning (DL), and emerging approaches, including transformer networks and large language models (LLMs), highlighting their contributions and potential. We also showcase real-world applications of these technologies and provide insights to guide their continued development. Each section details relevant technologies and methodologies and concludes with an analysis of challenges and limitations, identifying directions for future research.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101876"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023233","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}

引用次数: 0

Graph neural networks for IoT security: A comparative study 图神经网络用于物联网安全：比较研究

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.iot.2025.101863

Nicola Capuano , Vincenzo Carletti , Pasquale Foggia , Francesco Rosa , Mario Vento

{"title":"Graph neural networks for IoT security: A comparative study","authors":"Nicola Capuano , Vincenzo Carletti , Pasquale Foggia , Francesco Rosa , Mario Vento","doi":"10.1016/j.iot.2025.101863","DOIUrl":"10.1016/j.iot.2025.101863","url":null,"abstract":"<div><div>The increasing deployment of IoT devices has introduced new cybersecurity vulnerabilities, as traditional defense mechanisms often fail to protect resource-constrained and highly heterogeneous environments. Network traffic analysis has emerged as a key strategy for detecting malicious activities; however, the inherent dynamism of IoT communications undermines the effectiveness of traditional security mechanisms. In this paper, we focus on detecting malicious activities in IoT networks by solving a node-classification problem in a graph-based network representation. We evaluate six Graph Neural Network methods, encompassing both static and time-dependent models, using two distinct graph representations of network traffic. Our analysis is conducted across three recent IoT traffic datasets, and considers multiple snapshot durations to understand how temporal granularity affects detection accuracy. Through extensive experiments, we assess the impact of graph structure, snapshot duration, and temporal modeling on detection performance. Results show that GNNs, especially static models, are effective at identifying anomalous nodes even in unseen environments. We find that shorter snapshot durations consistently improve model accuracy by reducing noise in node embeddings, and that simpler traffic representation often match or outperform more complex counterparts, particularly when computational efficiency is a concern. Additionally, further research is needed to draw firm conclusions about dynamic methods. Our findings provide actionable insights for selecting models, representations, and configurations in the design of GNN-based intrusion detection systems for IoT networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101863"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885161","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}

引用次数: 0

An e-health environment conceived with the support of a self-adaptive IoT architecture 在自适应物联网架构的支持下构思的电子医疗环境

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-03 DOI: 10.1016/j.iot.2025.101844

Mateus G. do Nascimento, José Maria N. David, Mario A.R. Dantas, Regina Braga, Victor Ströele

{"title":"An e-health environment conceived with the support of a self-adaptive IoT architecture","authors":"Mateus G. do Nascimento, José Maria N. David, Mario A.R. Dantas, Regina Braga, Victor Ströele","doi":"10.1016/j.iot.2025.101844","DOIUrl":"10.1016/j.iot.2025.101844","url":null,"abstract":"<div><div>IoT has gradually exposed society to intelligent environments. Software developed for these environments requires efficient data processing, low response time, and proper functioning of sensors, devices, and systems. To meet these software requirements, we can leverage edge, fog, and cloud computing. However, the use of these computational resources presents challenges for software engineering, such as determining which architectures to employ for developing software in intelligent environments. Considering these challenges, this work addresses the research question: How can a self-adaptive architecture support automated computational resource allocation in e-health environments? To answer this research question, we propose a self-adaptive IoT architecture that uses artificial intelligence to manage computational resource usage in intelligent environments, enabling the management of physical spaces and ensuring the correct functioning of applications. A case study was conducted in an e-health environment to support our arguments. The Design Science Research methodology was used to develop the research, and its execution cycles in a real e-health corporate environment, through a case study, enabled the incremental construction of the architecture. The results demonstrate that the proposed architecture enhances the efficiency of allocating computational resources - encompassing edge, fog, and cloud computing - while ensuring the functioning of applications and supporting the management of the physical environment using artificial intelligence. As contributions, the study shows: (i) the self-adaptive architecture construction phases; (ii) how architecture adapts to the demands of the IoT intelligent environment; (iii) how artificial intelligence can support the allocation of computational resources.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101844"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791859","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}

引用次数: 0

Machine learning on the edge for sustainable IoT networks: A systematic literature review 可持续物联网网络边缘的机器学习：系统文献综述

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-12 DOI: 10.1016/j.iot.2025.101846

Luisa Schuhmacher , Jimmy Fernandez Landivar , Ihsane Gryech , Hazem Sallouha , Michele Rossi , Sofie Pollin

{"title":"Machine learning on the edge for sustainable IoT networks: A systematic literature review","authors":"Luisa Schuhmacher , Jimmy Fernandez Landivar , Ihsane Gryech , Hazem Sallouha , Michele Rossi , Sofie Pollin","doi":"10.1016/j.iot.2025.101846","DOIUrl":"10.1016/j.iot.2025.101846","url":null,"abstract":"<div><div>The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges, such as high energy consumption and inefficient resource management. Addressing these issues is critical for the long-term viability of IoT networks. Machine learning (ML), with its proven success across various domains, offers promising solutions for optimizing IoT operations. ML algorithms can learn directly from raw data, uncovering hidden patterns and optimizing processes in dynamic environments. Executing ML at the edge of IoT networks can further enhance sustainability by reducing bandwidth usage, enabling real-time decision-making, and improving data privacy. Additionally, testing ML models on actual hardware is essential to ensure satisfactory performance under real-world conditions, as it captures the complexities and constraints of real-world IoT deployments. Combining ML at the edge and actual hardware testing, therefore, increases the reliability of ML models to effectively improve the sustainability of IoT systems. The present systematic literature review explores how ML can be utilized to enhance the sustainability of IoT networks, examining current methodologies, benefits, challenges, and future opportunities. Through our analysis, we aim to provide insights that will drive future innovations in making IoT networks more sustainable.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101846"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791861","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}

引用次数: 0

Energy-efficient throughput optimization in UAV-based microservice networks for rural connectivity scenarios 农村互联场景下基于无人机的微业务网络节能吞吐量优化

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.iot.2026.101880

José Gómez-delaHiz , Aymen Fakhreddine , Jaime Galán-Jiménez

{"title":"Energy-efficient throughput optimization in UAV-based microservice networks for rural connectivity scenarios","authors":"José Gómez-delaHiz , Aymen Fakhreddine , Jaime Galán-Jiménez","doi":"10.1016/j.iot.2026.101880","DOIUrl":"10.1016/j.iot.2026.101880","url":null,"abstract":"<div><div>The research community is currently exploring the use of Unmanned Aerial Vehicle (UAV) networks to address coverage challenges in rural and economically disadvantaged regions. By equipping UAVs with small cells, coverage can be improved in areas where network operators are not prone to invest due to low Return on Investment. If there is a requirement from users in rural scenarios to achieve higher throughput (for instance, users seeking IoT services with stringent Quality-of-Service requirements), deploying multiple UAVs in the same area could be an effective strategy. However, this approach would also result in increased energy consumption. This paper addresses the challenge of maximizing the throughput offered in rural areas for users accessing microservice-based IoT applications, while also minimizing the energy consumption of UAV swarms. To achieve this, an optimal solution is proposed through a Mixed Integer Linear Programming (MILP) model, which is evaluated within realistic environments. Since this placement problem is complex due to its NP hard nature, in order to obtain solutions for large scenarios in tractable times, we also present a genetic algorithm (GA) that obtains results close to those reported by the MILP with a remarkable reduction in the computation time. Specifically, the optimality gap of the proposed GA-based solution is on average 2.32%, with a reduction of 89.92% in the computation time.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101880"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077989","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}

引用次数: 0

A spatio-temporal deep learning-based decision support system for energy awareness in IoT-based smart buildings 基于物联网的智能建筑能源意识时空深度学习决策支持系统

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-29 DOI: 10.1016/j.iot.2025.101856

Berna Cengiz, Resul Das

{"title":"A spatio-temporal deep learning-based decision support system for energy awareness in IoT-based smart buildings","authors":"Berna Cengiz, Resul Das","doi":"10.1016/j.iot.2025.101856","DOIUrl":"10.1016/j.iot.2025.101856","url":null,"abstract":"<div><div>The increasing demand for energy and rising expectations for user comfort necessitate the more accurate and efficient management of climate control systems in smart buildings. A crucial step in this process is reliably predicting indoor temperature. In this study, multivariate time series data, including environmental parameters such as temperature, Relative Humidity (RH), light, and Heating, Ventilating and Air Conditioning (HVAC) consumption, were used to evaluate the performance of various deep learning models. Hybrid approaches integrating Recurrent Neural Networks (RNN) architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, with Graph Convolutional Networks (GCN)(GCN-RNN, GCN-LSTM, GCN-GRU) were systematically compared. Furthermore, the Transformer architecture and the Extreme Gradient Boosting (XGBoost) algorithm were included in the comparison as a baseline reference. The results show that the GCN-GRU model achieved superior accuracy compared to other models in the analyzed regions and throughout the test period, reaching an <em>R</em><sup>2</sup> score of 0.9976 with low error rates and providing consistent accuracy. Beyond model performance, a user-friendly interface has been developed that enables the selection of alternative models, interactive visualization of prediction results, examination of the impact of the current control strategy on energy efficiency, and dynamic integration of new algorithms, thanks to a modular software architecture. These findings emphasize the importance of jointly processing temporal and spatial patterns and provide a practical foundation for decision support systems aimed at enhancing energy awareness and operational sustainability in IoT-enabled smart buildings.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101856"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926744","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}

引用次数: 0

Internet of Things Technologies for Occupational Health and Safety: A Review 面向职业健康与安全的物联网技术综述

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.iot.2026.101886

Sotiris Zikas , Theodor Panagiotakopoulos , Yiannis Kiouvrekis

{"title":"Internet of Things Technologies for Occupational Health and Safety: A Review","authors":"Sotiris Zikas , Theodor Panagiotakopoulos , Yiannis Kiouvrekis","doi":"10.1016/j.iot.2026.101886","DOIUrl":"10.1016/j.iot.2026.101886","url":null,"abstract":"<div><div>The Internet of Things (IoT) has significantly enhanced Occupational Health and Safety (OHS) by enabling real-time monitoring of workplace environments and employee well-being through smart sensors and connected devices. IoT systems help identify hazards, improve response times, and ensure compliance with safety regulations. Despite these advancements, the literature highlights a gap in investigating the technologies and algorithms specifically tailored for OHS applications. This paper presents a comprehensive review of IoT OHS technologies, structured around a four-tier IoT OHS system architecture, which serves as a framework for analyzing existing studies in the field. Our findings indicate that sensors from the perception tier are the most widely utilized component across IoT OHS implementations, underscoring their critical role in hazard detection and safety monitoring. In contrast, IoT cloud technologies remain underutilized, suggesting potential barriers to widespread adoption. From a machine learning (ML) perspective, supervised learning and classification models emerge as the dominant approaches, with classification models being employed in studies that incorporate all four sensor categories. In contrast, unsupervised learning models are infrequently applied in IoT OHS systems, indicating a preference for structured, labeled data analysis. Notably, accident prevention applications, particularly those addressing collisions and falls from height, leverage the full spectrum of ML approaches and sensor technologies, making them the most comprehensive category within the domain. By categorizing sensors, controllers, gateways, communication technologies, and cloud platforms, alongside analyzing ML methodologies, this paper provides valuable insights into the current landscape of IoT OHS systems and highlights key trends, challenges, and opportunities for future research.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101886"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173167","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}

引用次数: 0