During the pandemic of Corona-virus Disease 2019 (COVID-19), the whole world was confronted by a particularly high death toll and infection rate. Research has shown that air pollution plays a considerable part in the spread of certain illnesses and diseases. In the case of the COVID-19 pandemic, research has shown that increased air pollution has a negative effect on people's well-being and plays a role in the quick spread of the disease. Air pollution by itself affects the respiratory system of individuals which is aggravated, in addition, by a COVID19 infection. Some efforts have been made to use emerging technologies to combat the virus and its subsequent aerosol aspects to reduce transmission. In this context, we present an IoT system for Air Quality (AQ) monitoring and prediction using deep learning for data analysis and Augmented Reality (AR) for data visualization. The proposed system shows great potential for using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) units as a framework for leveraging knowledge from time-series data of AQ. Moreover, integrating AR visualization for the proposed IoT system enables intuitive interaction between users and IoT devices and further improves visualization of AQ data which effectively contributes to easily conducting a deeper analysis of data and makes faster decisions. © 2021 IEEE.
Deep Learning and Augmented Reality for IoT-based Air Quality Monitoring and Prediction System