Predicting Air Quality using Moving Sensors (poster)

In recent years, interest in measuring air quality has spiked due to rising environmental and health concerns in South Korea. In particular, microfine dust (microdust) is known to cause serious health issues to people. Therefore, measuring and predicting mircodust is an important problem. A typical way of measuring microdust is to use sensors from fixed location. However, this cannot capture the local dynamics of microdust and is limited to accurate measurement near fixed locations. Therefore, there is an immediate need to provide more accurate local air quality measurements in the areas where fixed local sensors are not installed. In this preliminary research, we focus on modeling the air quality pattern in a given local area by using vehicles equipped with cheap IoT sensors, where vehicles move around the area. As a pilot study, We measured the microdust level running experiments for 2 weeks with 3 different cars. Also, we developed an machine learning algorithm to better predict the local air quality using moving sensors. Further, we built an application where measured air quality is reported to the end users. We demonstrated the feasibility of using inexpensive IoT sensors in moving vehicles to provide better local air quality to end users.