Brito, ThadeuLima, JoséBiondo, EliasNakano, Alberto YoshiroPereira, Ana I.2024-01-052024-01-052023Brito, Thadeu; Lima, José; Biondo, Elias; Nakano, Alberto; Pereira, Ana I. (2023). A neural network approach in WSN real-time monitoring system to measure indoor air quality. In 3rd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). 27-28 September 2023, Cairo, Egypt. ISBN 979-8-3503-0623-1. p. 233-238979-8-3503-0623-1http://hdl.handle.net/10198/29102Indoor Air Quality (IAQ) pertains to the air quality within a specific space and is directly linked to the well-being and comfort of its occupants. In line with this objective, this research presents a real-time system dedicated to monitoring and predicting IAQ, encompassing both thermal comfort and gas concentration. The system initiates with a data acquisition, wherein a set of sensors captures environmental parameters and transmits this data for storage in a database. The measured parameters are analyzed by a neural network algorithm that predicts anomalies based on historical data. The neural network model generated predictions from 75.9% to 98.1% (depending on the parameter) of precision during regular situations. After that, a test with smoke in the same place was done to validate the model, and the results showed it could detect anomalies. Finally, prediction data are stored in a new database and displayed on a dashboard for monitoring in real-time measured and prediction data.engInternet of thingsWireless sensor networkIndoor air qualityArtificial neural networkconference object10.1109/MIUCC58832.2023.10278362