Cloud-Assisted IoT-Enabled Smoke Monitoring System (e-Nose) Using Machine Learning Techniques

Abstract

E-nose is a self-controlled smoke detector developed using Arduino microcontroller and sensors. It is capable of sensing the smell in the ambiance via gas sensors and programmed to alert the smokers giving red signal with buzzer. Smoke detection algorithm facilitates the intelligent warning to the cigarette smokers. Nowadays, smokers’ count is increasing with a high-speed raising health and moral issues to the people especially in case of passive smoking at public places. Today “No Smoking” rules are only written, but to make these rules being followed this work would be a milestone. To implement e-Nose, a cigarette smoke detector with red/green LED and buzzer using Arduino uno board R3 is developed. MQ-2 gas sensor is used to detect cigarette smoke and combination of buzzer and LEDs is used to warn smokers. This work demonstrates the development of e-Nose and evaluation of the performance of developed product. In nutshell, this e-Nose which is cigarette smoke detector is designed to achieve high degree of awareness among public and to reduce the smokers’ count especially at public places. We detect the smoke through e-Nose and upload on the cloud. The cloud analysis is done using various machine learning techniques to categorize the smoke with pollution data. And result shows smoking percentage.