IoT Based Indoor Air Quality Monitoring and Prediction System using Machine Learning

Abstract

Air and good air quality are important for humans to carry out their everyday activities Bad indoor air quality ( will cause to human such as irritation of the skin, nose, and mouth, headaches, dizziness, and weakness Current concern found by author on indoor air quality in UMS’s FCI such as problem in monitoring indoor air quality condition that would eventually affect the respiratory system of a student or academic staff, no proper notify system for indoor air quality condition to alert student or academic staff and inadequate statistics on indoor air pollution index to make prediction and keep track the air pollution index level In this project, Exponential Smoothing is chosen as the research element, and it will be used for prediction purpose Author used rapid prototyping methodology where it is a model consists of phases that able to build, test and reworked as necessary until an acceptable prototype is accomplished in the project The goal of the proposed system is to develop a web based indoor air quality monitoring and prediction system with IoT devices that can monitor and do prediction on indoor air pollution index ( using Machine Learning Finally, the proposed system will display the monitoring air pollution index ( data from sensor and used it for prediction data chart for UMS academic staff