# Detection of Smoking in Indoor Environment Using Machine Learning
## 1 Introduction  
In modern society, most people mainly live indoors. Therefore, the importance of indoor air pollution has been continuously mentioned [1]. According to World Health Organization (WHO) research results, each year, 3.8 million people worldwide prematurely die from illnesses attributable to indoor air quality [2]. Moreover, the wake of recent events with COVID-19 is expected to increase the interest and importance of indoor living environments as living patterns change. As indoor air pollutants, there are various factors such as outdoor air inflow, cooking, smoking, building materials, etc. Among them, heating, cooking and smoking were reported as typical indoor air pollution produced by human activities with purpose [3]. However, cooking and heating are essential parts of our lives while indoor smoking is only the preference but adversely affects our health. According to WHO, indoor smoking exhausts fine particles 100 times higher than acceptable levels [4]. Additionally, the US Environmental Health Administration indicates a study found that when nicotine and a tobacco toxic substance combine with dust in the air, 40% remained even after 21 days. This long-term effect of indoor smoke is the evidence of possibility of even third-hand smoke. Nevertheless, cigarette sales in the first quarter of 2020 increased by 4.1% compared to last year. Compared to last year, sales of e-cigarettes decreased this year, while sales of tobacco increased. Thus, the tobacco cigarette ratio is 80% of total tobacco sales [5].  
In order to define the effect of tobacco emissions on indoor air quality, many studies are conducted while global air quality monitoring regulations are framed in each country to save the human andenvironment from harmful gases in the air [6]. Therefore, beside the air contamination issues of smoking, the other studies have been proceeded to identify and detect pollutants such as Total Volatile Organic Compounds (TVOC), Particulate Matter (PM), Carbon Dioxide (CO\({}_{2}\)), Nitrogen Oxide (NO\({}_{\text{X}}\)), etc. [7; 8; 9; 10]. When smoking indoors occurs, the aforementioned pollutants are produced and can be detected by various sensors. As previously announced through many studies, environmental sensors have been developed or utilized for accurate measurement of pollutants. However, the measurement of air quality is very difficult because the atmospheric environment is changed in real time. In addition, it is difficult to predict and prepare for the future of the atmospheric environment using outdated data or measurement methods. Nowadays all the cities in the world are integrated with Information and Communication Technology (ICT) and Internet of Things (IoT) technology to promote urban development. The IoT technology is used to manage and improve the cities which assert and service for the overall system This paper aims to detect pollution of indoor smoking in real time and accurately predict the pollution caused by indoor smoking and various general indoor air pollutions using ICT based sensors, which is relatively reasonable priced than existing real time detection method; camera, motion detector, etc. In this study, we were able to predict the smoking situation in the indoor environment by using non-intrusive environmental sensors. We experimented to predict the presence or absence of smoking in various general situations (cooking, heating, smoking). The problem with vision-based technology is that it is relatively expensive and the installation of surveillance cameras such as infrared sensors and motion sensors is rather difficult. Smoking detection using commercially available sensors is relatively easy and inexpensive to deploy, and existing installed sensors can be utilized. Therefore, we proposed a system that monitors the changing atmospheric environment in real time by combining ICT technology and pre-installed sensors.