A data-driven framework for air quality sensor networks

Abstract

In this article, we present our research vision of a framework for obtaining quality data in air quality monitoring networks using low-cost sensors (LCSs). The use of LCS networks is gaining increasing acceptance in many IoT air quality applications. However, data quality and reliability issues are a major barrier to widespread adoption, which means that the pre-processing tasks that are critical to achieving the required levels of data quality are crucial aspects of LCS network designs. The proposed framework takes advantage of a layered architecture, which has also proven useful in other fields, and from which we show the challenges and state-of-the-art techniques for obtaining quality data. In addition, we show its usefulness in application cases, including a real case with data measured by a LCS deployment measuring O 3 in the area of Barcelona, Spain.