Building Environment Monitoring System based on WiDo-Open Source IoT Node

The objective of this research is to design a sensor system for monitoring the environmental conditions within a building. The environmental parameters being monitored include temperature, humidity, illuminance, carbon dioxide gas levels, dust density, and noise. Temperature and humidity are measured using the DT-Sense Temperature & Humidity Sensor based on the SHT10. Illuminance is measured using the DT-Sense Light Sensor based on the TEMT6000. Carbon dioxide gas levels are detected using the DT-Sense Gas Sensor based on the MG-811. Dust density is assessed with the Sharp GP2Y1010F46 Optical Dust Sensor, and a Microphone Sensor Module based on KY-038 is utilized for noise detection. These sensors are connected to the WiDo-Open Source IoT Node, which acts as a microcontroller. It sends real-time data, which is then displayed on the serial monitor of the Arduino Integrated Development Environment (IDE). The results of testing and calibration of the DT-Sense Temperature & Humidity Sensor showed high linearity with an R2 value close to 1.00, with a temperature range of 8.80°C to 37.90°C and a temperature sensing error of ±0.30°C. The humidity sensing exhibited a mean absolute error (MAE) of 0.12% RH. The DT-Sense Light Sensor provided a high level of linearity $\mathrm{(R^{2}=9.98 \times 10^{-1})}$ within an illuminance range of 9.00Lux to 3600.00Lux, with a calibration factor of $0.12 \times 10^{-2} \mathrm{~V} / \mathrm{Lux}$. The Sharp GP2Y1010F46 Optical Dust Sensor displayed excellent linearity $\mathrm{(R^{2}=9.99 \times 10^{-1})}$ with a sensitivity of $5.91 \mathrm{~V} /(\mathrm{mg} / \mathrm{m}^{3})$. The Microphone Sensor Module for noise detection demonstrated strong linearity $\mathrm{(R^{2}=9.96 \times 10^{-}}.$ ${ }^{1}$), a calibration factor of $3.50 \mathrm{mV} / \mathrm{dB}$, and a mean absolute error of $1.46 \mathrm{~dB}$. However, it is worth noting that the DT-Sense Gas Sensor used for carbon dioxide detection showed some output anomalies.