[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117542-en":3,"doc-seo-117542-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117542,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Gas Leakage Detection Using Tiny Machine Learning","Gas leakage detection is essential in both industrial and residential environments, where immediate response is required to prevent hazards. Many conventional solutions rely on centralized processing, causing latency and limiting scalability under growing deployment needs. This study proposes an edge-based TinyML approach that runs inference directly on low-cost devices. An MLX90640 thermal camera and two optimized CNNs, MobileNetV1 and EfficientNet-B0, are deployed on Arduino Nano 33 BLE Sense.","electronics   \nArticle  \nGas Leakage Detection Using Tiny Machine Learning  \nMajda El Barkani 1,*, Nabil Benamar 1,2,, Hanae Talei 1 and Miloud Bagaa 3, *  \nCitation: El BarkaniEl Barkani, M.; Benamar, N.; Talei, H.; Bagaa, M. Gas Leakage Detection Using Tiny Machine Learning. Electronics 2024, 13, 4768. [https://doi.org/10.3390/](https://doi.org/10.3390/)  \nelectronics13234768  \nAcademic Editor: Marcin Witczak  \nReceived: 21 October 2024  \nRevised: 21 November 2024  \nAccepted: 26 November 2024  \nPublished: 2 December 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Science and Engineering, Al Akhawayn University, Hassan II Avenue, Ifrane 53000, Morocco; n.benamar@aui.ma (N.B.); [h.talei@aui.ma](h.talei@aui.ma) (H.T.)  \n2 School of Technology, Moulay Ismail University of Meknes, Meknes 50050, Morocco  \n3 Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivières, Trois-Rivieres, QC G8Z 4M3, Canada  \n* [Correspondence: m.elbarkani@aui.ma](Correspondence: m.elbarkani@aui.ma) (M.E.B.); [miloud.bagaa@uqtr.ca](miloud.bagaa@uqtr.ca) (M.B.)  \nAbstract: Gas leakage detection is a critical concern in both industrial and residential settings, where real-time systems are essential for quickly identifying potential hazards and preventing dangerous incidents. Traditional detection systems often rely on centralized data processing, which can lead to delays and scalability issues. To overcome these limitations, in this study, we present a solution based on tiny machine learning (TinyML) to process data directly on devices. TinyML has the potential to execute machine learning algorithms locally, in real time, and using tiny devices, such as microcontrollers, ensuring faster and more efficient responses to potential dangers. Our approach combines an MLX90640 thermal camera with two optimized convolutional neural networks (CNNs), MobileNetV1 and EfficientNet-B0, deployed on the Arduino Nano 33 BLE Sense. The results show that our system not only provides real-time analytics but does so with high accuracy—88.92% for MobileNetV1 and 91.73% for EfficientNet-B0—while achieving inference times of 1414 milliseconds and using just 124.8 KB of memory. Compared to existing solutions, our edge-based system overcomes common challenges related to latency and scalability, making it a reliable, fast, and efficient option. This work demonstrates the potential for low-cost, scalable gas detection systems that can be deployed widely to enhance safety in various environments. By integrating cutting-edge machine learning models with affordable IoT devices, we aim to make safety more accessible, regardless of financial limitations, and pave the way for further innovation in environmental monitoring solutions.  \nKeywords: CNN; cost-effective IoT solutions; EfficientNet-B0; IoT; MobileNetV1; TinyML  \n1. Introduction  \nThe Internet of Things (IoT) has emerged as a transformative technology that fundamentally reshapes our interaction with the digital world. By seamlessly integrating sensors and actuators into everyday objects, the IoT has enabled a new era of connectivity and real-time data collection in various sectors. From enhancing home automation to revolutionizing industrial operations, the deployment of IoT technologies promises improved efficiency and responsiveness in real-time applications [1] .  \nThe merging of AI with the Internet of Things (IoT) expands the potential of IoT systems. This connection enables advanced data analytics and decision-making processes at network edges, eliminating the latency and bandwidth constraints associated with cloud computing. 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