Internet of Things最新文献

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 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2026-01-07 DOI: 10.1016/j.iot.2025.101870

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

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.

现代物联网（IoT）系统配备了大量传感器，提供有关其组件当前运行的实时数据，这对系统的内部控制系统和流程至关重要。然而，这些数据通常过于细粒度，无法获得对物联网系统可能参与的更大流程执行的有用见解。流程挖掘为分析业务流程开发了先进的方法，这些方法也可以用于物联网。将流程挖掘引入物联网需要一个事件抽象步骤，将低级传感器数据提升到业务流程级别。在这项工作中，我们的目标是使领域专家能够使用一种名为Radiant的新开发的领域特定语言（DSL）来执行这一步骤。Radiant支持传感器数据中的模式规范，这些模式指示高级流程活动的执行。这些模式被转换为复杂事件处理（CEP）应用程序，用于在运行时检测活动执行。我们提出了一个相应的软件架构，可以使用CEP应用程序从物联网传感器流中进行在线事件抽象。我们评估这些应用程序，以监控智能制造和智能医疗保健中的活动执行情况。这些评估有助于告知领域专家关于基于指定模式的活动检测的质量，以及通过附加或修改的模式和传感器进行改进的潜力。

{"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":"2026-01-07","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

Analysis of microservices-based IoT systems: deployment challenges, industry practices, and performance insights 基于微服务的物联网系统分析：部署挑战、行业实践和性能洞察

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2026-01-07 DOI: 10.1016/j.iot.2025.101867

Yahia El Fellah , Jean Baptiste Minani , Naouel Moha , Julien Gascon-Samson , Yann-Gaël Guéhéneuc

As the adoption of microservices in Internet of Things (IoT) systems grows, deploying them on the Edge remains a significant challenge for practitioners. While Edge Computing offers improved latency and resource efficiency by processing data near the source, it also introduces complexity in managing microservices. Despite increasing academic interest, few comprehensive studies have investigated the specific challenges and effective software engineering (SE) practices for deploying microservices-based IoT systems on the Edge. Therefore, we conducted a multi-method study to bridge this gap. We used three methods: (1) a systematic literature review (SLR) to identify known challenges and approaches, (2) a gray literature review (GLR) to extract SE practices used in the field, and (3) an empirical evaluation using two versions of a case study, one with and one without selected SE practices. The findings show that (1) the most reported challenges relate to resource utilization and performance optimization, (2) containerized microservices, API gateways, and database-per-service are among the most commonly recommended practices, and (3) implementing these practices led to a 132% throughput improvement, 49% reduction in latency, and memory savings of up to 13% in Edge-based IoT systems. However, increased architectural complexity also led to higher CPU usage. This study offers a catalog of best practices and empirical evidence to support IoT developers aiming to optimize microservices-based deployments on the Edge, particularly in resource-constrained environments.

随着微服务在物联网（IoT）系统中的应用越来越多，在边缘部署它们仍然是从业者面临的一个重大挑战。虽然边缘计算通过在源附近处理数据提供了改进的延迟和资源效率，但它也引入了管理微服务的复杂性。尽管学术界越来越感兴趣，但很少有全面的研究调查了在边缘部署基于微服务的物联网系统的具体挑战和有效的软件工程（SE）实践。因此，我们进行了一项多方法研究来弥补这一差距。我们使用了三种方法：(1)系统文献综述（SLR）来确定已知的挑战和方法，(2)灰色文献综述（GLR）来提取该领域使用的SE实践，以及(3)使用两个版本的案例研究进行实证评估，一个有一个没有选定的SE实践。研究结果表明：(1)报告的最大挑战与资源利用和性能优化有关；(2)容器化微服务、API网关和每服务数据库是最常用的推荐实践；(3)在基于边缘的物联网系统中，实施这些实践可以提高132%的吞吐量，减少49%的延迟，节省高达13%的内存。然而，增加的体系结构复杂性也导致了更高的CPU使用率。本研究提供了一系列最佳实践和经验证据，以支持物联网开发人员优化基于微服务的边缘部署，特别是在资源受限的环境中。

{"title":"Analysis of microservices-based IoT systems: deployment challenges, industry practices, and performance insights","authors":"Yahia El Fellah , Jean Baptiste Minani , Naouel Moha , Julien Gascon-Samson , Yann-Gaël Guéhéneuc","doi":"10.1016/j.iot.2025.101867","DOIUrl":"10.1016/j.iot.2025.101867","url":null,"abstract":"<div><div>As the adoption of microservices in Internet of Things (IoT) systems grows, deploying them on the Edge remains a significant challenge for practitioners. While Edge Computing offers improved latency and resource efficiency by processing data near the source, it also introduces complexity in managing microservices. Despite increasing academic interest, few comprehensive studies have investigated the specific challenges and effective software engineering (SE) practices for deploying microservices-based IoT systems on the Edge. Therefore, we conducted a multi-method study to bridge this gap. We used three methods: (1) a systematic literature review (SLR) to identify known challenges and approaches, (2) a gray literature review (GLR) to extract SE practices used in the field, and (3) an empirical evaluation using two versions of a case study, one with and one without selected SE practices. The findings show that (1) the most reported challenges relate to resource utilization and performance optimization, (2) containerized microservices, API gateways, and database-per-service are among the most commonly recommended practices, and (3) implementing these practices led to a 132% throughput improvement, 49% reduction in latency, and memory savings of up to 13% in Edge-based IoT systems. However, increased architectural complexity also led to higher CPU usage. This study offers a catalog of best practices and empirical evidence to support IoT developers aiming to optimize microservices-based deployments on the Edge, particularly in resource-constrained environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101867"},"PeriodicalIF":7.6,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978121","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

AI for IoMT security: a comprehensive survey of intrusion detection and system architectures 人工智能物联网安全：入侵检测和系统架构的全面调查

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2026-01-06 DOI: 10.1016/j.iot.2025.101869

Mohammed Yacoubi , Omar Moussaoui , Cyril Drocourt

Recent advances in the Internet of Medical Things (IoMT) have significantly improved data processing and patient care within Smart Healthcare systems. However, these developments have also expanded the surface of potential cyber threats targeting sensitive medical infrastructures. To address these challenges, a variety of security approaches both traditional and Artificial Intelligence (AI)-based have been proposed to strengthen the resilience of IoMT environments. In particular, Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated strong capabilities in detecting and mitigating abnormal behaviors and malicious activities. This paper provides a comprehensive survey of recent AI-driven methods applied to IoMT security, with a particular focus on intrusion detection systems (IDS), the availability and characteristics of public datasets, and architectural considerations for deploying security solutions across Cloud, Fog, and Edge computing layers. The paper also discusses legal and ethical concerns related to data protection in healthcare contexts. Finally, the study outlines open challenges and future research directions for developing robust, adaptive, and trustworthy security frameworks in the IoMT ecosystem.

医疗物联网（IoMT）的最新进展显著改善了智能医疗保健系统中的数据处理和患者护理。然而，这些发展也扩大了针对敏感医疗基础设施的潜在网络威胁的范围。为了应对这些挑战，人们提出了各种传统和基于人工智能（AI）的安全方法，以加强物联网环境的弹性。特别是机器学习（ML）和深度学习（DL）技术在检测和减轻异常行为和恶意活动方面表现出了强大的能力。本文对最近应用于IoMT安全的人工智能驱动方法进行了全面调查，特别关注入侵检测系统（IDS）、公共数据集的可用性和特征，以及跨云、雾和边缘计算层部署安全解决方案的架构考虑。本文还讨论了与医疗保健环境中的数据保护相关的法律和伦理问题。最后，该研究概述了在IoMT生态系统中开发健壮、自适应和可信赖的安全框架的开放挑战和未来研究方向。

{"title":"AI for IoMT security: a comprehensive survey of intrusion detection and system architectures","authors":"Mohammed Yacoubi , Omar Moussaoui , Cyril Drocourt","doi":"10.1016/j.iot.2025.101869","DOIUrl":"10.1016/j.iot.2025.101869","url":null,"abstract":"<div><div>Recent advances in the Internet of Medical Things (IoMT) have significantly improved data processing and patient care within Smart Healthcare systems. However, these developments have also expanded the surface of potential cyber threats targeting sensitive medical infrastructures. To address these challenges, a variety of security approaches both traditional and Artificial Intelligence (AI)-based have been proposed to strengthen the resilience of IoMT environments. In particular, Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated strong capabilities in detecting and mitigating abnormal behaviors and malicious activities. This paper provides a comprehensive survey of recent AI-driven methods applied to IoMT security, with a particular focus on intrusion detection systems (IDS), the availability and characteristics of public datasets, and architectural considerations for deploying security solutions across Cloud, Fog, and Edge computing layers. The paper also discusses legal and ethical concerns related to data protection in healthcare contexts. Finally, the study outlines open challenges and future research directions for developing robust, adaptive, and trustworthy security frameworks in the IoMT ecosystem.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101869"},"PeriodicalIF":7.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978119","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

HiFEL-OCKT: Hierarchical federated edge learning with objective congruence and multi-level knowledge transfer for IoT ecosystems HiFEL-OCKT：物联网生态系统中具有客观同余和多层次知识转移的分层联邦边缘学习

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2026-01-03 DOI: 10.1016/j.iot.2025.101868

Ahmed-Rafik Baahmed, Jean-François Dollinger, Mohamed El Amine Brahmia, Mourad Zghal

The explosive growth of Internet of Things (IoT) data and the demand for real-time decisions necessitate edge intelligence to overcome the latency and bandwidth limitations of cloud-only processing. Real-world IoT ecosystems are characterized by their high heterogeneity, which results from a wide variety of devices, sensors, environments, data, tasks, and resources, posing significant communication and computation efficiency challenges, scalability issues, and privacy concerns for edge intelligence. We propose HiFEL-OCKT, a novel hierarchical federated edge learning methodology for addressing the realistic high heterogeneity of IoT ecosystems, while enabling efficient edge intelligence. The key novelty of our proposed HiFEL-OCKT methodology is the efficient and scalable deployment of temporal intelligence at the edge by exploiting the valuable knowledge flowing at this level, which we define with the learning objective evolution, to ensure robust edge personalization through objective congruent collaboration and multi-level knowledge transfer between IoT devices. Through extensive experiments on multiple IoT domains, including smart buildings and industrial IoT with heterogeneous real-world datasets, our HiFEL-OCKT approach uncovered the novel ability in collaborating various highly heterogeneous IoT devices from different ecosystem settings. Our approach demonstrates superior performance and efficiency compared to the state-of-the-art approaches, with an improvement rate as high as 87.57 % in the edge knowledge personalization, while achieving significant speedups as high as 4.38 ×  in local training.

物联网（IoT）数据的爆炸式增长和对实时决策的需求需要边缘智能来克服仅云处理的延迟和带宽限制。现实世界的物联网生态系统具有高度异质性的特点，这是由各种各样的设备、传感器、环境、数据、任务和资源造成的，对边缘智能提出了重大的通信和计算效率挑战、可扩展性问题和隐私问题。我们提出了HiFEL-OCKT，这是一种新的分层联邦边缘学习方法，用于解决物联网生态系统的现实高异质性，同时实现高效的边缘智能。我们提出的HiFEL-OCKT方法的关键新颖之处在于，通过利用这一层次上有价值的知识流动，在边缘有效和可扩展地部署时间智能，我们用学习目标进化来定义这一层次，通过客观一致的协作和物联网设备之间的多层次知识转移来确保强大的边缘个性化。通过对多个物联网领域的广泛实验，包括智能建筑和工业物联网与异构现实世界数据集，我们的HiFEL-OCKT方法揭示了协作来自不同生态系统设置的各种高度异构物联网设备的新能力。与最先进的方法相比，我们的方法表现出卓越的性能和效率，在边缘知识个性化方面的改进率高达87.57%，同时在局部训练方面实现了高达4.38 × 的显著加速。

{"title":"HiFEL-OCKT: Hierarchical federated edge learning with objective congruence and multi-level knowledge transfer for IoT ecosystems","authors":"Ahmed-Rafik Baahmed, Jean-François Dollinger, Mohamed El Amine Brahmia, Mourad Zghal","doi":"10.1016/j.iot.2025.101868","DOIUrl":"10.1016/j.iot.2025.101868","url":null,"abstract":"<div><div>The explosive growth of Internet of Things (IoT) data and the demand for real-time decisions necessitate edge intelligence to overcome the latency and bandwidth limitations of cloud-only processing. Real-world IoT ecosystems are characterized by their high heterogeneity, which results from a wide variety of devices, sensors, environments, data, tasks, and resources, posing significant communication and computation efficiency challenges, scalability issues, and privacy concerns for edge intelligence. We propose HiFEL-OCKT, a novel hierarchical federated edge learning methodology for addressing the realistic high heterogeneity of IoT ecosystems, while enabling efficient edge intelligence. The key novelty of our proposed HiFEL-OCKT methodology is the efficient and scalable deployment of temporal intelligence at the edge by exploiting the valuable knowledge flowing at this level, which we define with the learning objective evolution, to ensure robust edge personalization through objective congruent collaboration and multi-level knowledge transfer between IoT devices. Through extensive experiments on multiple IoT domains, including smart buildings and industrial IoT with heterogeneous real-world datasets, our HiFEL-OCKT approach uncovered the novel ability in collaborating various highly heterogeneous IoT devices from different ecosystem settings. Our approach demonstrates superior performance and efficiency compared to the state-of-the-art approaches, with an improvement rate as high as 87.57 % in the edge knowledge personalization, while achieving significant speedups as high as 4.38 ×  in local training.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101868"},"PeriodicalIF":7.6,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926730","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 enhanced group matching method with transformed fingerprints, similarity filtering, and adaptive selection for indoor localization 基于指纹变换、相似度滤波和自适应选择的分组匹配方法

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2026-01-01 DOI: 10.1016/j.iot.2025.101866

Huang Lin , Shuo Li , Wei Peng , Arthur Peng

WiFi fingerprinting-based indoor localization faces persistent challenges from signal instability and environmental interference. To address these issues, this study introduces an enhanced Group Matching Method with Transformed Fingerprints, Similarity Filtering, and Adaptive Selection, (GMM-TSFA), that improves localization accuracy, interpretability, and robustness. GMM-TSFA integrates three core mechanisms: instance-wise transformation to normalize signal variations and strengthen similarity metrics; similarity filtering to dynamically refine the candidate pool based on signal relevance; and adaptive selection to flexibly determine the most informative reference points. Together, these components improve both pattern recognition and spatial precision. Experiments on four benchmark datasets demonstrate the effectiveness of the proposed method, with a mean localization error (MLE) of 7.30 m on UJIIndoorLoc and 77.86 % of errors under 10 m, consistently outperforming baseline methods including the original GMM. Unlike deep learning-based models, GMM-TSFA offers a transparent and adaptable framework that supports explainable decision-making. These advantages make it a practical and scalable solution for real-world indoor localization systems deployed in complex environments.

基于WiFi指纹的室内定位面临着信号不稳定和环境干扰的持续挑战。为了解决这些问题，本研究引入了一种基于转换指纹、相似度滤波和自适应选择的增强组匹配方法（GMM-TSFA），提高了定位精度、可解释性和鲁棒性。GMM-TSFA集成了三个核心机制：实例化转换以标准化信号变化并加强相似性度量；相似度滤波，基于信号相关性动态细化候选库；并自适应选择，灵活确定信息量最大的参考点。这些组件共同提高了模式识别和空间精度。在4个基准数据集上的实验证明了该方法的有效性，在UJIIndoorLoc上的平均定位误差（MLE）为7.30 m， 10 m以下的误差为77.86%，始终优于包括原始GMM在内的基准方法。与基于深度学习的模型不同，GMM-TSFA提供了一个透明和适应性强的框架，支持可解释的决策。这些优点使其成为在复杂环境中部署的真实室内定位系统的实用且可扩展的解决方案。

{"title":"An enhanced group matching method with transformed fingerprints, similarity filtering, and adaptive selection for indoor localization","authors":"Huang Lin , Shuo Li , Wei Peng , Arthur Peng","doi":"10.1016/j.iot.2025.101866","DOIUrl":"10.1016/j.iot.2025.101866","url":null,"abstract":"<div><div>WiFi fingerprinting-based indoor localization faces persistent challenges from signal instability and environmental interference. To address these issues, this study introduces an enhanced Group Matching Method with Transformed Fingerprints, Similarity Filtering, and Adaptive Selection, (GMM-TSFA), that improves localization accuracy, interpretability, and robustness. GMM-TSFA integrates three core mechanisms: instance-wise transformation to normalize signal variations and strengthen similarity metrics; similarity filtering to dynamically refine the candidate pool based on signal relevance; and adaptive selection to flexibly determine the most informative reference points. Together, these components improve both pattern recognition and spatial precision. Experiments on four benchmark datasets demonstrate the effectiveness of the proposed method, with a mean localization error (MLE) of 7.30 m on UJIIndoorLoc and 77.86 % of errors under 10 m, consistently outperforming baseline methods including the original GMM. Unlike deep learning-based models, GMM-TSFA offers a transparent and adaptable framework that supports explainable decision-making. These advantages make it a practical and scalable solution for real-world indoor localization systems deployed in complex environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101866"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978120","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

Real-time scalable UAV condition monitoring framework with hardware-level acceleration for IoT applications 具有物联网应用硬件级加速的实时可扩展无人机状态监测框架

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-30 DOI: 10.1016/j.iot.2025.101865

Antonios Ntib , Dimitrios Michael Manias , Abdallah Shami

Unmanned Aerial Vehicles (UAVs) are a critical component of emerging Smart Cities, supporting applications such as emergency response, transportation, environmental monitoring, and infrastructure inspection. Ensuring their reliability requires condition monitoring frameworks capable of real-time defect detection despite the UAVs’ resource-constrained nature. This work presents a real-time, scalable UAV condition monitoring framework that efficiently distributes computation between core servers and edge nodes. The novelty of this work lies in two main contributions: (i) the design and realization of a deployable framework tailored for real-time UAV monitoring with offloading capabilities to both edge and core resources, and (ii) the integration of hardware-level acceleration strategies, including OpenMP-based parallelization and AVX2 SIMD vectorization, to substantially enhance computational efficiency, scalability, and real-time feasibility. Together, these contributions position the framework as a practical solution ready for large-scale UAV swarm deployments. The overall improvements include significant reductions in processing time and enhanced resource utilization while maintaining predictive performance. A comparative evaluation across three frameworks, a baseline state-of-the-art Python framework, an intermediate C++/Cython translation, and the proposed fully optimized OpenMP/AVX2-based framework, demonstrates the framework’s readiness for integration into critical UAV-enabled IoT systems.

无人驾驶飞行器（uav）是新兴智慧城市的关键组成部分，支持应急响应，运输，环境监测和基础设施检查等应用。尽管无人机的资源有限，但确保其可靠性需要能够实时检测缺陷的状态监测框架。这项工作提出了一个实时、可扩展的无人机状态监测框架，该框架有效地在核心服务器和边缘节点之间分配计算。这项工作的新颖之处在于两个主要贡献：(i)设计和实现了为无人机实时监控定制的可部署框架，具有边缘和核心资源的卸载能力；（ii）集成了硬件级加速策略，包括基于openmp的并行化和AVX2 SIMD矢量化，以大幅提高计算效率、可扩展性和实时可行性。总之，这些贡献将该框架定位为大规模无人机群部署的实用解决方案。总体改进包括显著减少处理时间和提高资源利用率，同时保持预测性能。对三个框架的比较评估，一个最先进的基准Python框架，一个中间的c++ /Cython翻译，以及提议的完全优化的基于OpenMP/ avx2的框架，证明了该框架准备好集成到关键的无人机支持的物联网系统中。

{"title":"Real-time scalable UAV condition monitoring framework with hardware-level acceleration for IoT applications","authors":"Antonios Ntib , Dimitrios Michael Manias , Abdallah Shami","doi":"10.1016/j.iot.2025.101865","DOIUrl":"10.1016/j.iot.2025.101865","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) are a critical component of emerging Smart Cities, supporting applications such as emergency response, transportation, environmental monitoring, and infrastructure inspection. Ensuring their reliability requires condition monitoring frameworks capable of real-time defect detection despite the UAVs’ resource-constrained nature. This work presents a real-time, scalable UAV condition monitoring framework that efficiently distributes computation between core servers and edge nodes. The novelty of this work lies in two main contributions: (i) the design and realization of a deployable framework tailored for real-time UAV monitoring with offloading capabilities to both edge and core resources, and (ii) the integration of hardware-level acceleration strategies, including OpenMP-based parallelization and AVX2 SIMD vectorization, to substantially enhance computational efficiency, scalability, and real-time feasibility. Together, these contributions position the framework as a practical solution ready for large-scale UAV swarm deployments. The overall improvements include significant reductions in processing time and enhanced resource utilization while maintaining predictive performance. A comparative evaluation across three frameworks, a baseline state-of-the-art Python framework, an intermediate C++/Cython translation, and the proposed fully optimized OpenMP/AVX2-based framework, demonstrates the framework’s readiness for integration into critical UAV-enabled IoT systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101865"},"PeriodicalIF":7.6,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885162","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 edge-intelligent three-tier framework for real-time forest fire detection, integrating WSNs, WMSNs, and UAVs 一种边缘智能三层框架，用于实时森林火灾探测，集成了wsn、wmsn和无人机

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-29 DOI: 10.1016/j.iot.2025.101861

Hamzeh Abu Ali , Enver Ever , Burak Kizilkaya , Muhammad Toaha Raza Khan , Masood Ur Rehman , Shuja Ansari , Muhammad Ali Imran , Adnan Yazici

Forest fires are becoming prevalent, threatening ecosystems, economies, and public safety while creating an urgent demand for rapid and reliable detection systems. Conventional approaches such as watchtowers, manual patrols, and satellite imaging suffer from limited coverage, delays, and inadequate precision. To address these challenges, we propose a three-tier, edge-centric framework that integrates wireless sensor networks (WSNs), wireless multimedia sensor networks (WMSNs), unmanned aerial vehicles (UAVs), and lightweight machine learning (ML) and deep learning (DL) models for efficient detection. In the first tier, scalar sensors provide early hazard identification; in the second, smart sensors execute a lightweight ML model for intermediate verification, achieving a 94% F1-score with a minimal feature set; and in the third, UAVs equipped with sensors, cameras, and a compact convolutional neural network (CNN) deliver final confirmation. The CNN achieves state-of-the-art results with a 100% F1 score on the FireMan-UAV-RGBT dataset and 99.5% on UAV-FFDB while remaining compact (1.6 MB) and efficient (157 ms inference on Raspberry Pi 5), enabling real-time edge deployment. Simulations show reduced end-to-end delay (813.59 ms) compared to WSN-only (865.84 ms) and WMSN (1066.18 ms) baselines, improved throughput (7.05 kbps vs 3.80 kbps and 3.06 kbps), and a 100% delivery ratio. Real-world WSN testbed experiments further validate the framework, achieving a 97% delivery ratio, 144.39 ms latency (vs. 258.37 ms in simulations), and energy consumption of 0.0559 J/s (closely matching 0.0442 J/s in simulations). These results collectively demonstrate the practicality and effectiveness of the framework for real-time forest fire monitoring and rapid emergency response.

森林火灾越来越普遍，威胁着生态系统、经济和公共安全，同时迫切需要快速可靠的检测系统。传统的方法，如瞭望塔、人工巡逻和卫星成像，受到覆盖范围有限、延迟和精度不足的困扰。为了应对这些挑战，我们提出了一个三层、以边缘为中心的框架，该框架集成了无线传感器网络（wsn）、无线多媒体传感器网络（wmsn）、无人机（uav）以及轻量级机器学习（ML）和深度学习（DL）模型，以实现高效检测。在第一层，标量传感器提供早期危险识别；其次，智能传感器执行轻量级ML模型进行中间验证，以最小的特征集实现94%的f1得分；第三，配备传感器、摄像头和紧凑卷积神经网络（CNN）的无人机提供最终确认。CNN在FireMan-UAV-RGBT数据集上获得了100%的F1分数，在UAV-FFDB上获得了99.5%的分数，同时保持了紧凑（1.6 MB）和高效（在Raspberry Pi 5上推断157 ms），实现了实时边缘部署。仿真显示，与纯wsn （865.84 ms）和WMSN （1066.18 ms）基线相比，端到端延迟（813.59 ms）减少，吞吐量提高（7.05 kbps vs 3.80 kbps和3.06 kbps），传输率达到100%。真实WSN测试平台实验进一步验证了该框架，实现了97%的传输率、144.39 ms的延迟（模拟为258.37 ms）和0.0559 J/s的能耗（与模拟中的0.0442 J/s非常接近）。这些结果共同证明了该框架在森林火灾实时监测和快速应急响应方面的实用性和有效性。

{"title":"An edge-intelligent three-tier framework for real-time forest fire detection, integrating WSNs, WMSNs, and UAVs","authors":"Hamzeh Abu Ali , Enver Ever , Burak Kizilkaya , Muhammad Toaha Raza Khan , Masood Ur Rehman , Shuja Ansari , Muhammad Ali Imran , Adnan Yazici","doi":"10.1016/j.iot.2025.101861","DOIUrl":"10.1016/j.iot.2025.101861","url":null,"abstract":"<div><div>Forest fires are becoming prevalent, threatening ecosystems, economies, and public safety while creating an urgent demand for rapid and reliable detection systems. Conventional approaches such as watchtowers, manual patrols, and satellite imaging suffer from limited coverage, delays, and inadequate precision. To address these challenges, we propose a three-tier, edge-centric framework that integrates wireless sensor networks (WSNs), wireless multimedia sensor networks (WMSNs), unmanned aerial vehicles (UAVs), and lightweight machine learning (ML) and deep learning (DL) models for efficient detection. In the first tier, scalar sensors provide early hazard identification; in the second, smart sensors execute a lightweight ML model for intermediate verification, achieving a 94% F1-score with a minimal feature set; and in the third, UAVs equipped with sensors, cameras, and a compact convolutional neural network (CNN) deliver final confirmation. The CNN achieves state-of-the-art results with a 100% F1 score on the FireMan-UAV-RGBT dataset and 99.5% on UAV-FFDB while remaining compact (1.6 MB) and efficient (157 ms inference on Raspberry Pi 5), enabling real-time edge deployment. Simulations show reduced end-to-end delay (813.59 ms) compared to WSN-only (865.84 ms) and WMSN (1066.18 ms) baselines, improved throughput (7.05 kbps vs 3.80 kbps and 3.06 kbps), and a 100% delivery ratio. Real-world WSN testbed experiments further validate the framework, achieving a 97% delivery ratio, 144.39 ms latency (vs. 258.37 ms in simulations), and energy consumption of 0.0559 J/s (closely matching 0.0442 J/s in simulations). These results collectively demonstrate the practicality and effectiveness of the framework for real-time forest fire monitoring and rapid emergency response.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101861"},"PeriodicalIF":7.6,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885164","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

Neuromorphic solar edge AI for sustainable wildfire detection 用于可持续野火探测的神经形态太阳边缘AI

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-29 DOI: 10.1016/j.iot.2025.101862

Raúl Parada

This paper presents a feasibility study of a solar-autonomous wildfire detection system using neuromorphic edge AI on fixed-wing drones. Through a comprehensive year-long simulation over Parc del Garraf (Catalonia), we evaluate three edge computing platforms, Raspberry Pi 4, Google Coral TPU, and BrainChip Akida, integrated into solar-optimized eBee X drones. Results show that the BrainChip Akida achieves 4200 patrol hrs per yr, nearly three times that of traditional CPU systems, while maintaining 87 % solar energy autonomy. The Google Coral TPU and Raspberry Pi 4 reach 66 % and 52 % autonomy, respectively. Fleet scaling analysis demonstrates that increasing drone count from one to eight reduces median wildfire detection time from 18 to 2.2 hrs, surpassing critical response thresholds. Seasonal analysis reveals Akida-based systems can operate fully on solar energy during summer and most of spring and fall, minimizing grid dependency. These findings establish neuromorphic computing as a foundational technology for sustainable, perpetual environmental monitoring within the Internet of Robotic Things (IoRT).

本文研究了一种基于神经形态边缘人工智能的太阳能自主野火探测系统在固定翼无人机上的可行性。通过对Parc del Garraf （Catalonia）进行为期一年的全面模拟，我们评估了三个边缘计算平台，树莓派4，谷歌Coral TPU和BrainChip Akida，集成到太阳能优化的eBee X无人机中。结果表明，BrainChip Akida实现了每年4200小时的巡逻时间，几乎是传统CPU系统的三倍，同时保持了87%的太阳能自主性。谷歌Coral TPU和Raspberry Pi 4分别达到66%和52%的自主性。机队规模分析表明，将无人机数量从1架增加到8架，将野火探测时间的中位数从18小时减少到2.2小时，超过了关键响应阈值。季节性分析显示，秋田系统可以在夏季和春季和秋季的大部分时间完全依靠太阳能运行，从而最大限度地减少对电网的依赖。这些发现确立了神经形态计算作为机器人物联网（IoRT）中可持续、永久环境监测的基础技术。

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 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-29 DOI: 10.1016/j.iot.2025.101856

Berna Cengiz, Resul Das

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 R² 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.

日益增长的能源需求和对用户舒适度的期望使得智能建筑中气候控制系统的管理更加准确和有效。这一过程的关键一步是可靠地预测室内温度。在这项研究中，多变量时间序列数据，包括环境参数，如温度，相对湿度（RH），光，采暖，通风和空调（HVAC）消耗，用于评估各种深度学习模型的性能。将包括长短期记忆（LSTM）和门控循环单元（GRU）模型在内的循环神经网络（RNN）架构与图卷积网络（GCN）（GCN-RNN、GCN-LSTM、GCN-GRU）相结合的混合方法进行了系统比较。此外，变压器架构和极限梯度增强（XGBoost）算法被纳入比较作为基准参考。结果表明，GCN-GRU模型在分析区域和整个测试期间的精度优于其他模型，R2得分为0.9976，错误率低，准确度一致。除了模型性能，还开发了一个用户友好的界面，可以选择替代模型，预测结果的交互式可视化，检查当前控制策略对能源效率的影响，以及新算法的动态集成，这要归功于模块化的软件架构。这些发现强调了联合处理时空模式的重要性，并为决策支持系统提供了实践基础，旨在提高物联网智能建筑的能源意识和运营可持续性。

{"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":"2025-12-29","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

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 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-26 DOI: 10.1016/j.iot.2025.101863

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

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.

物联网设备的不断增加部署带来了新的网络安全漏洞，因为传统的防御机制往往无法保护资源受限和高度异构的环境。网络流量分析已成为检测恶意活动的关键策略；然而，物联网通信固有的动态性破坏了传统安全机制的有效性。在本文中，我们专注于通过解决基于图的网络表示中的节点分类问题来检测物联网网络中的恶意活动。我们评估了六种图神经网络方法，包括静态和时间依赖模型，使用两种不同的网络流量图表示。我们的分析是在三个最近的物联网流量数据集上进行的，并考虑了多个快照持续时间，以了解时间粒度如何影响检测准确性。通过大量的实验，我们评估了图结构、快照持续时间和时间建模对检测性能的影响。结果表明，即使在不可见的环境中，gnn，特别是静态模型，也能有效地识别异常节点。我们发现，更短的快照持续时间通过减少节点嵌入中的噪声不断提高模型的准确性，并且更简单的流量表示通常匹配或优于更复杂的对应，特别是当计算效率是一个问题时。此外，还需要进一步的研究来得出关于动态方法的确切结论。我们的研究结果为在物联网网络中基于gnn的入侵检测系统设计中选择模型、表示和配置提供了可操作的见解。

{"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":"2025-12-26","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