TY - JOUR
T1 - Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador
AU - Alvear, Vanessa
AU - Burbano, Yadira
AU - Rosero, Paul
AU - Tözün, Pinar
AU - Marcillo, Fabricio
AU - Wilmar Hernandez
PY - 2022/9/13
Y1 - 2022/9/13
N2 - Nowadays, increasing air-pollution levels are a public health concern that affects all living beings, with the most polluting gases being present in urban environments. For this reason, this research presents portable Internet of Things (IoT) environmental monitoring devices that can be installed in vehicles and that send message queuing telemetry transport (MQTT) messages to a server, with a time series database allocated in edge computing. The visualization stage is performed in cloud computing to determine the city air-pollution concentration using three different labels: low, normal, and high. To determine the environmental conditions in Ibarra, Ecuador, a data analysis scheme is used with outlier detection and supervised classification stages. In terms of relevant results, the performance percentage of the IoT nodes used to infer air quality was greater than 90%. In addition, the memory consumption was 14 Kbytes in a flash and 3 Kbytes in a RAM, reducing the power consumption and bandwidth needed in traditional air-pollution measuring stations.
AB - Nowadays, increasing air-pollution levels are a public health concern that affects all living beings, with the most polluting gases being present in urban environments. For this reason, this research presents portable Internet of Things (IoT) environmental monitoring devices that can be installed in vehicles and that send message queuing telemetry transport (MQTT) messages to a server, with a time series database allocated in edge computing. The visualization stage is performed in cloud computing to determine the city air-pollution concentration using three different labels: low, normal, and high. To determine the environmental conditions in Ibarra, Ecuador, a data analysis scheme is used with outlier detection and supervised classification stages. In terms of relevant results, the performance percentage of the IoT nodes used to infer air quality was greater than 90%. In addition, the memory consumption was 14 Kbytes in a flash and 3 Kbytes in a RAM, reducing the power consumption and bandwidth needed in traditional air-pollution measuring stations.
KW - Internet-of-Things
KW - air quality
KW - Machine Learning
KW - data analysis
KW - Environmental sustainability
UR - https://www.mdpi.com/1424-8220/22/18/7015
U2 - https://doi.org/10.3390/s22187015
DO - https://doi.org/10.3390/s22187015
M3 - Journal article
VL - 22
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 18
M1 - 7015
ER -