Name
Abstract | ||
|---|---|---|
Improvements in small sized sensors allow us to easily detect the presence of Volatile Organic Compounds (VOCs) in the air using easy-to-deploy Internet of Things (IoT)-scale devices. However, classifying what VOC exists in the environment still remains as a complex task. Knowing what VOCs are in the air can help us remove the main cause that vents VOC materials in order to maintain clean air quality. In this work, we present VOCkit, an IoT sensor kit for non-chemical experts to easily detect and classify different types of VOCs. VOCkit combines miniature chemically-designed fluorometric sensors for recognizing VOCs with an embedded imaging system for classification. Exposing the fluorometric sensors with various VOCs, result in photophysical property change of each fluorescent compound, which composes the sensors, and the synergistic combination of the changes create unique individual fluorescent color patterns respectively to the VOC material. The fluorescent color change pattern is captured using a camera and the images are processed with machine learning algorithms on the embedded platform for VOC classification. Using 500 fluorometric sensor images collected for five different commonly contactable VOCs, we show the feasibility of performing VOC classification on small-sized IoT devices. For the VOC types of our interest, our results show a classification accuracy of 97%, implying the potential applicability of VOCkit for real-world usage. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DCOSS.2019.00034 | 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) |
Keywords | Field | DocType |
Internet of Things Application,Machine Learning,Volatile Organic Compound Classification | Material classification,Computer science,Internet of Things,Real-time computing,Air quality index,Distributed computing | Conference |
ISSN | ISBN | Citations |
2325-2936 | 978-1-7281-0571-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jungmo Ahn | 1 | 4 | 2.92 |
| Hyungi Kim | 2 | 1 | 1.36 |
| Eunha Kim | 3 | 9 | 1.99 |
| JeongGil Ko | 4 | 674 | 64.60 |