Intelligent Safety-Redundant IoT-Edge System for Indoor Gas Leakage Detection
Issued Date
2026-01-01
Resource Type
ISSN
18650929
eISSN
18650937
Scopus ID
2-s2.0-105011967428
Journal Title
Communications in Computer and Information Science
Volume
2380 CCIS
Start Page
122
End Page
136
Rights Holder(s)
SCOPUS
Bibliographic Citation
Communications in Computer and Information Science Vol.2380 CCIS (2026) , 122-136
Suggested Citation
Kachanun V., Mueangthongkham K., Pantaraksakul P., Moolkaew J., Scully P.M.D., Tamkittikhun N., Tantidham T., Hu C.L. Intelligent Safety-Redundant IoT-Edge System for Indoor Gas Leakage Detection. Communications in Computer and Information Science Vol.2380 CCIS (2026) , 122-136. 136. doi:10.1007/978-981-96-6291-3_10 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114616
Title
Intelligent Safety-Redundant IoT-Edge System for Indoor Gas Leakage Detection
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
This paper presents the development of an intelligent gas leak detection system designed to enhance safety in residential household environments. The system integrates an MQ-2 gas sensor, a Raspberry Pi V4 (24 GFLOPs), with Camera Module, and AHT20 sensor to monitor gas concentrations, detect smoke, and track temperature conditions. Real-time alerts are triggered through local buzzers and instant messaging notifications when hazardous conditions are identified. Image object detection model, trained on a dataset of fire and smoke images, aids in detecting smoke and fire in the vicinity. Five test-cases were designed to simulate real-world household conditions in order to test the safety-redundancy of the decision system in isolation. In 71% of the simulated real-world household conditions the alert response is issued correctly; with precision at 0.68 for the sensor-based gas leak decision system. At 1.1 GFLOPs the SSD MobileNetV2 FPNLite model (preliminarily) fine-trained on the FASD [7] dataset reached 43.6% mAP50; a suitable time complexity level for on-device or on-microcontroller operation. Performance is compared to the YOLOv8 model (8.7 GFLOPs). The preliminary experimental results demonstrated the system's utility to detect indicators for indoor flammable gases, fire and smoke hazards. Further field testing is necessary to assess its performance. This implementation provides an affordable and scalable solution for gas leak detection, offering added safety-redundancy.