## 2 Implementation of the System
This enhances the system's adaptability across diverse measurement scenarios and increases the reliability and accuracy of air quality monitoring. As part of developing an air quality monitoring system, data from the BME680 and CCS811 sensors was integrated using optimal fusion parameters to ensure high accuracy and reliability of monitoring [38]. The strategy was to exploit the synergy between the two sensors. The BME680 provided important temperature and humidity data, while the CCS811 focused on measuring VOC concentrations and equivalent CO\({}_{2}\) levels. This combination of data provided a more complete picture of IAQ. To implement data fusion effectively, an algorithm integrating measurements from both sensors was developed, considering their timestamps to synchronize the data [39]. The system analyzes the collected data using machine learning techniques, such as neural networks, to identify relationships between humidity, temperature, VOC, and CO\({}_{2}\) parameters. This approach enables the accurate determination of air pollutant levels and the prediction of potential changes in air quality based on the dynamics of these parameters. The integration process considers the significance of normalization and pre-processing of input data to ensure compatibility and compliance with the analytical model's requirements. The use of filtering and normalization helps eliminate potential data distortions and improve the accuracy of the system's conclusions. The integration results demonstrate that using data from multiple sensors improves the accuracy of air quality measurements. In order to achieve the optimal data fusion process, a weighted averaging technique was employed, whereby data from the BME680 and CCS811 sensors were combined in order to leverage the strengths of each sensor. The multi-parameter readings provided by the BME680 were enhanced by data obtained from the CCS811, thus facilitating a more comprehensive understanding of IAQ. The fusion algorithm assigns weights based on the accuracy and relevance of each sensor's data for specific air quality parameters, thereby ensuring that the final readings reflect the most reliable and pertinent information. The efficacy of these algorithms was verified through extensive testing across diverse residential environments. The effectiveness of the moving average filter was validated by comparing raw sensor data with filtered data, demonstrating a clear reduction in noise and transient anomalies without sacrificing significant trends. Our machine learning model processes the sensor data to recognize patterns and correlations that may not be immediately apparent through simple observation. This analysis enables the system to provide more meaningful and actionable insights into indoor air quality, thereby facilitating proactive air quality management. This machine learning-based approach is a key element of the system's intellectualization, transforming it from an ordinary measuring device into an intelligent air quality management tool. \begin{table}
\begin{tabular}{c c c} \hline
**Air Indicator** & **Maximal Possible Error** & **Measurement Ranges** \\ \hline Atmospheric pressure & \(\pm\)1 hPa & 300–1100 hPa \\ CO\({}_{2}\) & \(\pm\)3\% & 400–8192 ppm \\ Humidity & \(\pm\)3\% & 0–100\% \\ Temperature & \(\pm\)1.0 \({}^{\circ}\)C & \(-\)40–85 \({}^{\circ}\)C \\ VOC & \(\pm\)3\% & 0–11,870 ppb \\ \hline \end{tabular}
\end{table} 
Table 2: The maximum possible measurement deviations of the air quality indicators. The integration of BME680 and CCS811 sensor data in a fusion air quality monitoring system is a crucial step toward developing an advanced solution capable of providing an accurate and comprehensive assessment of air quality in residential areas. The use of advanced data processing methods significantly enhances the system's potential to detect and respond to changes in air quality, contributing to a healthier and safer home environment [40].