Internet of Things最新文献_第6页

WiAR : Wi-Fi-based human activity recognition using time-frequency analysis and lightweight deep learning for smart environments WiAR：基于wi - fi的人类活动识别，用于智能环境的时频分析和轻量级深度学习

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-28 DOI: 10.1016/j.iot.2026.101881

Vamsi Krishna Puduru , Rakesh Reddy Yakkati , Sreenivasa Reddy Yeduri , Sagar Koorapati , Linga Reddy Cenkeramaddi

{"title":"WiAR : Wi-Fi-based human activity recognition using time-frequency analysis and lightweight deep learning for smart environments","authors":"Vamsi Krishna Puduru , Rakesh Reddy Yakkati , Sreenivasa Reddy Yeduri , Sagar Koorapati , Linga Reddy Cenkeramaddi","doi":"10.1016/j.iot.2026.101881","DOIUrl":"10.1016/j.iot.2026.101881","url":null,"abstract":"<div><div>Recognizing human activities in smart environments has significant usage in home automation, security, healthcare monitoring, etc. This paper proposes WiAR, which is a Wi-Fi-based human activity recognition method using Continuous Wavelet Transform (CWT) and lightweight Convolutional Neural Networks (CNNs). The proposed approach is evaluated on the IEEE 802.11ax Channel State Information (CSI) dataset. First, WiAR utilizes the CWT to generate the spectrogram images from the CSI extracted from Wi-Fi signals for different activities: walking, running, staying in place, and empty space. Then, these spectrogram images are processed with a CNN to classify these activities efficiently. Experimental results show that the proposed WiAR achieves an accuracy of approximately 91.1% when compared to various pre-trained models such as DenseNet, EfficientNet, MobileNet, ResNet, and VGGNet. Finally, the proposed CNN model is deployed on various edge computing devices, including Raspberry Pi 5, to validate its real-time implementation in terms of inference time.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101881"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077993","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

Cross-network cross-interface relaying via LoRa-ZigBee synergy: Enabling energy-efficient delay-constrained communication across low-power IoT networks 通过LoRa-ZigBee协同实现跨网络跨接口中继：在低功耗物联网网络中实现高能效延迟约束通信

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.iot.2026.101878

Hua Qin , Ni Li , Gelan Yang , Yang Peng

{"title":"Cross-network cross-interface relaying via LoRa-ZigBee synergy: Enabling energy-efficient delay-constrained communication across low-power IoT networks","authors":"Hua Qin , Ni Li , Gelan Yang , Yang Peng","doi":"10.1016/j.iot.2026.101878","DOIUrl":"10.1016/j.iot.2026.101878","url":null,"abstract":"<div><div>The rapid expansion of the Internet of Things (IoT) ecosystem has propelled widespread deployment of distributed low-power wireless networks, among which ZigBee stands out for diverse innovative applications. However, energy-efficient cross-network communication remains challenging, as existing solutions like multi-hop ZigBee and one-hop LoRa entail trade-offs between communication delays and deployment costs. To tackle these issues, we propose a novel cross-interface relaying paradigm that utilizes a star topology within each ZigBee network and designates a relay node with an additional LoRa interface to bridge networks via a central LoRa gateway. Compared with existing methods, this approach reduces energy consumption and costs while improving scalability. To implement this paradigm while balancing energy conservation with delay guarantees, we introduce a Cross-network Cross-interface Relaying (CCR) scheme, which jointly schedules LoRa and ZigBee transmission behaviors to minimize energy consumption under delay constraints. CCR uses a scheduling framework that breaks down end-to-end delay constraints into link-level constraints, enabling global optimization of transmission parameters and dynamic adaptation to link quality variations. The effectiveness of CCR is demonstrated through extensive field tests on a prototype implemented on Raspberry Pi 3B+. Results show that CCR reduces energy consumption by 55.4% and 39.1% compared with an advanced LoRa communication protocol and a state-of-the-art cross-interface relaying scheme, respectively, while ensuring that 98.7% of packets satisfy their delay constraints. These findings highlight the potential of CCR for efficient and reliable cross-network communication in large-scale IoT deployments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101878"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023232","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 game-theoretic approach to sustainable smart manufacturing using IoT under policy incentives: a case study of Iran and South Korea 政策激励下利用物联网实现可持续智能制造的博弈论方法：以伊朗和韩国为例

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.2025.101860

Mahnaz Naghsh-Nilchi , Morteza Rasti-Barzoki , Mohammad-Bagher Jamali , Jörn Altmann , Bernhard Egger

{"title":"A game-theoretic approach to sustainable smart manufacturing using IoT under policy incentives: a case study of Iran and South Korea","authors":"Mahnaz Naghsh-Nilchi , Morteza Rasti-Barzoki , Mohammad-Bagher Jamali , Jörn Altmann , Bernhard Egger","doi":"10.1016/j.iot.2025.101860","DOIUrl":"10.1016/j.iot.2025.101860","url":null,"abstract":"<div><div>This study presents a mathematical model to analyze the competition between smart and traditional home appliance manufacturers in Iran and South Korea using a game theory approach. The smart manufacturer utilizes IoT-enabled products and a Sensing-as-a-Service (SaaS) ecosystem to collect and analyze real-time operational data, enabling predictive maintenance, energy optimization, and intelligent product design. This data-driven approach extends the product life cycle, reduces operating costs, and reduces material waste and carbon emissions. In contrast, the traditional manufacturer relies on conventional R&D and after-sales feedback, which limits responsiveness and efficiency. The proposed model is the first to highlight the strategic value of data analytics in the SaaS ecosystem in shaping competitive advantage, sustainability, and market performance by considering customer behavior and government incentives. The results show that providing targeted incentives for data sharing significantly increases demand, profitability, and environmental benefits, especially in mature digital markets. Furthermore, the study shows that supporting context-specific policies enhances the effectiveness of smart manufacturing strategies. Overall, this research provides actionable insights for policymakers, manufacturers, and IoT stakeholders seeking to foster sustainable, competitive, and digital industrial systems in emerging and advanced economies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101860"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885163","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

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.iot.2025.101869

Mohammed Yacoubi , Omar Moussaoui , Cyril Drocourt

{"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":"2026-03-01","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

μTEE: Certification-ready RISC-V platform for secure IIoT lifecycle management μTEE：用于安全工业物联网生命周期管理的认证就绪RISC-V平台

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.iot.2026.101885

Alessandro Cilardo , Jesús García-Rodríguez , Jose Manuel Merlos Espín , Roberto Nardone

{"title":"μTEE: Certification-ready RISC-V platform for secure IIoT lifecycle management","authors":"Alessandro Cilardo , Jesús García-Rodríguez , Jose Manuel Merlos Espín , Roberto Nardone","doi":"10.1016/j.iot.2026.101885","DOIUrl":"10.1016/j.iot.2026.101885","url":null,"abstract":"<div><div>The digital transformation of industrial environments increasingly relies on resource-constrained Industrial Internet of Things (IIoT) devices, which must operate securely despite limited computational capabilities and hostile deployment conditions. Ensuring their trustworthiness is essential for complying with emerging regulations such as the EU Cyber-Resilience Act and for achieving certification under standards such as IEC 62443. This paper presents <em>μ</em>TEE, a lightweight Trusted Execution Environment for RISC-V microcontroller-class platforms, explicitly designed to support certification-ready lifecycle security in ultra-lightweight IIoT nodes. <em>μ</em>TEE combines hardware-enforced isolation, a secure root of trust, and enclave-based modularity for trusted system services, with an efficient symmetric-cryptography framework tailored to constrained devices. Beyond the architectural contribution, we perform a detailed IEC 62443 Security Level 3 compliance analysis, providing one of the first systematic demonstrations of how a constrained IIoT platform can be evaluated against this standard. The full compliance checklist is released as supplementary material, offering a practical methodology for applying IEC 62443 in novel IIoT contexts. We further illustrate <em>μ</em>TEE’s integration into lifecycle management flows and demonstrate its deployment in a PLC-based smart metering scenario. A prototypical FPGA implementation on an AMD-Xilinx Artix-7 platform shows modest area and performance costs, with limited overhead on application throughput. Comparative evaluation highlights <em>μ</em>TEE’s advantages over software-only TEEs, Arm TrustZone-M, and baseline RISC-V PMP approaches. The results confirm that <em>μ</em>TEE delivers a certifiable, open-hardware trust anchor for IIoT, and that it serves as a replicable case study of IEC 62443 compliance evaluation, advancing the secure deployment of Industry 4.0 infrastructures.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101885"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173269","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

TinyHAR-UQ: Battery-aware, uncertainty-controlled tinyML for wearable activity recognition on IoT edge devices TinyHAR-UQ：电池感知、不确定性控制的tinyML，用于物联网边缘设备上的可穿戴活动识别

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.iot.2026.101889

Ismail Lamaakal , Chaymae Yahyati , Yassine Maleh , Khalid El Makkaoui , Ibrahim Ouahbi

{"title":"TinyHAR-UQ: Battery-aware, uncertainty-controlled tinyML for wearable activity recognition on IoT edge devices","authors":"Ismail Lamaakal , Chaymae Yahyati , Yassine Maleh , Khalid El Makkaoui , Ibrahim Ouahbi","doi":"10.1016/j.iot.2026.101889","DOIUrl":"10.1016/j.iot.2026.101889","url":null,"abstract":"<div><div>Tiny, battery-powered wearables increasingly run human activity recognition (HAR) models, yet they rarely know when to trust predictions or when to stop computing to save energy. We address this gap with <em>TinyHAR-UQ</em>, a microcontroller-ready HAR pipeline that turns int8 networks into risk-aware, energy-aware predictors. Our contributions are threefold. First, we realize on-device conformal prediction sets for HAR by combining an int8 temporal backbone, early exits, Dirichlet (evidential) heads, and a streaming integer-only conformal layer that enforces user-chosen miscoverage levels. Second, we design a budget-aware controller that, at test time, decides whether to exit early, continue, or abstain based on prediction-set size and resource constraints. Third, we propose micro-temperature personalization, a zero-cost calibration scheme (two scalars per exit folded into quantized scales/biases) that recovers calibration lost to quantization. Deployed on Cortex-M4F and ESP32-S3 devices and evaluated on four HAR benchmarks, <em>TinyHAR-UQ</em> achieves nominal 90-95% coverage with near-singleton sets while cutting median latency and energy by up to 50% and 20-30% versus a matched single-exit baseline, enabling reliable, efficient HAR on off-the-shelf IoT edge platforms.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101889"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173271","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

Scalable and low-power edge architecture with Wi-Fi HaLow and on-device spectrograms generation for flexible urban bioacoustics monitoring 可扩展和低功耗边缘架构，具有Wi-Fi HaLow和设备上的频谱生成，用于灵活的城市生物声学监测

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.iot.2025.101864

Francisco A. Delgado-Rajó , Carlos M. Travieso-González , Ruyman Hernández-López

{"title":"Scalable and low-power edge architecture with Wi-Fi HaLow and on-device spectrograms generation for flexible urban bioacoustics monitoring","authors":"Francisco A. Delgado-Rajó , Carlos M. Travieso-González , Ruyman Hernández-López","doi":"10.1016/j.iot.2025.101864","DOIUrl":"10.1016/j.iot.2025.101864","url":null,"abstract":"<div><div>Urban biodiversity monitoring in smart cities requires scalable and efficient computing architectures capable of handling real-time, distributed sensing tasks. This paper proposes a low-power edge computing and Internet of Things (IoT) framework that enables on-device acoustic detection and classification of bird species, serving as bioindicators of ecosystem health. The architecture leverages lightweight convolutional neural networks (CNNs) deployed on energy-efficient sensor nodes, significantly reducing communication overhead by transmitting only detection events and compact spectrogram data. A key contribution is the automatic generation of Mel-spectrograms at the edge, which supports the continuous creation of training datasets and iterative neural network refinement without manual preprocessing. The proposed system incorporates dual Wi-Fi and Wi-Fi HaLow connectivity, providing adaptable long-range, low-power communication for heterogeneous urban environments. Field experiments validate the framework’s scalability and effectiveness, demonstrating robust detection of both native and invasive species. By combining distributed intelligence, resource-aware computation, and flexible networking, the system offers a practical edge–IoT solution for large-scale, real-time environmental monitoring in smart city contexts.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101864"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885165","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

SCENE: Serving cluster formation in cEll-free dyNamic environments 场景：在无细胞动态环境中服务集群形成

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.2026.101873

Marco Silva , José Santos , Marília Curado , Chan-Tong Lam , Benjamin K. Ng

{"title":"SCENE: Serving cluster formation in cEll-free dyNamic environments","authors":"Marco Silva , José Santos , Marília Curado , Chan-Tong Lam , Benjamin K. Ng","doi":"10.1016/j.iot.2026.101873","DOIUrl":"10.1016/j.iot.2026.101873","url":null,"abstract":"<div><div>This paper introduces SCENE (Serving Cluster formation in cEll-free dyNamic Environments), a novel optimization model for serving cluster formation in Cell-Free massive Multiple-Input Multiple-Output networks. SCENE addresses the challenge of supporting heterogeneous service requirements - including critical Internet of Things (IoT) and latency-sensitive services as well as regular broadband applications - under dynamic network conditions. Unlike traditional approaches that rely on iterative refinement algorithms, SCENE performs one-shot serving cluster formation, eliminating the overhead of successive optimization loops. This innovation leads to significantly lower computational complexity and faster execution times while preserving service quality. Simulation results show that SCENE achieves superior performance in both average and 90%-likely spectral efficiency compared to state-of-the-art baselines, while demonstrating strong robustness under varying traffic profiles and pilot scarcity. SCENE enables efficient, scalable, and service-aware cluster formation, making it a promising candidate for dynamic and heterogeneous 6G and IoT environments</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101873"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077988","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

Adaptive artificial noise beamforming for securing grant-free massive machine-type communication 自适应人工噪声波束形成，确保大规模机器型通信免授权

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-21 DOI: 10.1016/j.iot.2025.101859

Uchenna P. Enwereonye, Ahmad Salehi Shahraki, Hooman Alavizadeh, A S M Kayes

{"title":"Adaptive artificial noise beamforming for securing grant-free massive machine-type communication","authors":"Uchenna P. Enwereonye, Ahmad Salehi Shahraki, Hooman Alavizadeh, A S M Kayes","doi":"10.1016/j.iot.2025.101859","DOIUrl":"10.1016/j.iot.2025.101859","url":null,"abstract":"<div><div>Massive machine-type communication (mMTC) growth beyond 5G (B5G)/6G networks presents significant security challenges, particularly in grant-free scenarios where traditional cryptographic methods are insufficient. The lack of defined access control and the complexities of key management expose these systems to vulnerabilities such as pilot contamination and jamming. Existing physical layer security (PLS) techniques, including adaptive beamforming and artificial noise generation, are limited by their static nature and reliance on perfect channel state information (CSI), making them less effective in the dynamic and densely populated environments characteristic of mMTC. This paper introduces Adaptive Artificial Noise Beamforming (AANB), an enhanced PLS approach designed to optimise the trade-off between security and system performance for grant-free mMTC in B5G/6G, by dynamically adjusting beamforming vectors and artificial noise based on real-time CSI and spatial correlation, while ensuring minimal impact on legitimate users and maximising interference against eavesdroppers. The proposed AANB’s secrecy outage probability (SOP) for grant-free mMTC is analytically derived, and the impact of AANB is demonstrated through simulations, which shows that AANB significantly lowers SOP when benchmarked against Semi-grant-free (SGF) and traditional beamforming with artificial noise (BF+AN) techniques in grant-free mMTC environment. The results indicate that AANB consistently outperforms SGF and BF+AN, achieving lower SOP values across various signal-to-noise ratio (SNR) levels and spatial correlation scenarios, offering robust security in grant-free mMTC scenarios. Additionally, bit error rate (BER) analysis demonstrates that AANB consistently outperforms benchmark schemes across all SNR levels, due to its adaptive noise-to-signal ratio optimisation, thereby enhancing resistance against eavesdropping in grant-free mMTC.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101859"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841106","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

Smart parking with pixel-wise ROI selection for vehicle detection using YOLOv8, YOLOv9, YOLOv10, and YOLOv11 使用YOLOv8， YOLOv9， YOLOv10和YOLOv11进行车辆检测，具有逐像素ROI选择的智能停车

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-19 DOI: 10.1016/j.iot.2025.101858

Gustavo P C P da Luz, Gabriel Massuyoshi Sato, Luis Fernando Gomez Gonzalez, Juliana Freitag Borin

{"title":"Smart parking with pixel-wise ROI selection for vehicle detection using YOLOv8, YOLOv9, YOLOv10, and YOLOv11","authors":"Gustavo P C P da Luz, Gabriel Massuyoshi Sato, Luis Fernando Gomez Gonzalez, Juliana Freitag Borin","doi":"10.1016/j.iot.2025.101858","DOIUrl":"10.1016/j.iot.2025.101858","url":null,"abstract":"<div><div>The increasing urbanization and the growing number of vehicles in cities have underscored the need for efficient parking management systems. Traditional smart parking solutions often rely on sensors or cameras for occupancy detection, each with its limitations. Recent advancements in deep learning have introduced new YOLO models (YOLOv8, YOLOv9, YOLOv10, and YOLOv11), but these models have not been extensively evaluated in the context of smart parking systems, particularly when combined with Region of Interest (ROI) selection for object detection. Existing methods still rely on fixed polygonal ROI selections or simple pixel-based modifications, which limit flexibility and precision. This work introduces a novel approach that integrates Internet of Things, Edge Computing, and Deep Learning concepts, by using the latest YOLO models for vehicle detection. By exploring both edge and cloud computing, it was found that inference times on edge devices ranged from 1 to 92 seconds, depending on the hardware and model version. Additionally, a highly flexible pixel-wise post-processing ROI selection method is proposed for accurately identifying regions of interest to count vehicles in parking lot images, overcoming the limitations of conventional polygon-based approaches. The proposed system achieved 99.68 % balanced accuracy on a custom dataset of 3484 images, providing a cost-effective smart parking solution that ensures precise vehicle detection while preserving data privacy, improving upon the previously used method by over 20 percentage points while maintaining the inference at the edge.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101858"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841198","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