IoT-Driven classroom air quality management with deep hierarchical cluster analysis

Area of Science:

• Environmental Science
• Computer Science

Background:

• Poor classroom air quality and high dust levels negatively impact student health, comfort, and academic performance.
• Effective monitoring and prediction systems are needed to mitigate these adverse effects.

Purpose of the Study:

• To develop an Internet of Things (IoT) enabled system for real-time monitoring and prediction of classroom air quality and dust levels.
• To identify key factors influencing indoor air quality and dust accumulation in educational environments.

Main Methods:

• Utilized multiple sensors for collecting classroom air quality data.
• Applied deep hierarchical cluster analysis to identify patterns and trends in large datasets.
• Developed a Long Short-Term Memory (LSTM) model for accurate prediction of air quality and dust levels based on clustered data.

Main Results:

• Deep hierarchical cluster analysis effectively revealed hidden patterns in air quality and dust data.
• The developed LSTM model demonstrated accuracy in predicting classroom air quality and dust levels.
• The system proved suitable for real-time implementation and practical application in schools.

Conclusions:

• The proposed IoT system provides a viable solution for managing classroom air quality.
• Real-time monitoring and prediction can significantly contribute to a healthier learning environment.
• This approach has the potential to enhance student well-being and academic productivity.