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Advisor(s)
Abstract(s)
Indoor 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.
Description
Keywords
Internet of things Wireless sensor network Indoor air quality Artificial neural network
Citation
Brito, 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. 233-238
Publisher
IEEE