Machine Learning-Based Edge Intelligence for Reliable Gas Monitoring Networks

Gas-sensing systems and their networks play a critical role in ensuring safety and environmental compliance across industrial and residential environments. Their reliability, however, depends on the rapid and accurate detection of gas leaks and abnormal gas levels. This article addresses the challenge of limited selectivity in gas sensor nodes by developing next generation, Internet of Things (IoT)-enabled air quality monitoring (AQM) nodes. These nodes leverage intelligent machine learning models to form an edge-assisted gas-sensing network (Ea-GNet). Using low-cost metal–oxide–semiconductor (MOS) chemical sensors, the proposed system enables selective detection and accurate identification of multiple gases from complex mixtures, followed by precise concentration estimation. Experimental results demonstrate a classification accuracy of approximately 98% on randomly mixed samples and an average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula>-score of 0.84 for volatile organic compounds (VOCs), such as acetone, ethanol, and methanol. In addition, the integration of edge computing significantly improves the processing efficiency, supporting minimal delays and enabling near-real-time monitoring. With the adopted simulation settings, the edge computing improved the throughput of the Ea-GNeT system by 634% compared with the cloud-based processing with fixed quality of service (QoS) settings. The latency of Ea-GNet is remarkably reduced from the order of 18 s–52 ms when the number of sensor nodes reaches 200. The findings highlight the viability of implementing intelligent machine learning models at the edge to improve the responsiveness and reliability of gas-sensing networks.