Research Article
A WSN Real-Time Monitoring System Approach for Measuring Indoor Air Quality Using the Internet of Things
@INPROCEEDINGS{10.1007/978-3-031-25222-8_7, author={Elias Biondo and Thadeu Brito and Alberto Nakano and Jos\^{e} Lima}, title={A WSN Real-Time Monitoring System Approach for Measuring Indoor Air Quality Using the Internet of Things}, proceedings={Internet of Everything. The First EAI International Conference, IoECon 2022, Guimar\"{a}es, Portugal, September 16-17, 2022, Proceedings}, proceedings_a={IOECON}, year={2023}, month={1}, keywords={Indoor Air Quality Monitoring System Internet of Things Wireless Sensor Network}, doi={10.1007/978-3-031-25222-8_7} }- Elias Biondo
Thadeu Brito
Alberto Nakano
José Lima
Year: 2023
A WSN Real-Time Monitoring System Approach for Measuring Indoor Air Quality Using the Internet of Things
IOECON
Springer
DOI: 10.1007/978-3-031-25222-8_7
Abstract
Indoor Air Quality (IAQ) describes the air quality of a room, and it refers to the health and comfort of the occupants. Typically, people spend around 90% of their time in indoor environments where the concentration of air pollutants and, occasionally, more than 100 times higher than outdoor levels. According to the World Health Organization (WHO), indoor air pollution is responsible for the death of 3.8 million people annually. It has been indicated that IAQ in residential areas or buildings is significantly affected by three primary factors, they are outdoor air quality, human activity in buildings, and building and construction materials. In this context, this work consists of a real-time IAQ system to monitor thermal comfort and gas concentration. The system has a data acquisition stage, captured by the WSN with a set of sensors that measures the data and send it to be stored on the InfluxDB database and displayed on Grafana. A Linear Regression (LR) algorithm was used to predict the behavior of the measured parameters, scoring up to 99.7% of precision. Thereafter, prediction data is stored on InfluxDB in a new database and displayed on Grafana. In this way, it is possible to monitor the actual measurement data and prediction data in real-time.
