Fog computing enabled air quality monitoring and prediction leveraging deep learning in IoT

• Published in: Journal of intelligent & fuzzy systems Vol. 43; no. 5; pp. 5621 - 5642
• Main Authors: Bharathi, P. Divya, Narayanan, V. Anantha, Sivakumar, P. Bagavathi
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2022
• Subjects: Air monitoring; Air quality; Atmospheric models; Cloud computing; Decision making; Deep learning; Forecasting; Mathematical models; Meteorological parameters; Network latency; Outdoor air quality; Performance prediction; Pollutants; Real time; Urbanization
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-212713

With the rapid industrialization and urbanization worldwide, air quality levels are deteriorating at an unprecedented rate and posing a substantial threat to humans and the environment. This brings the concern to effectively monitor and forecast air quality levels in real-time. Conventional air quality monitoring stations are built based on centralized architectures involving high latency, communication technologies demanding high power, sensors involving high costs and decision making with moderate accuracy. To address the limitations of the existing systems, we propose a smart and distinct Air Quality Monitoring and Forecasting system embracing Fog Computing with IoT and Deep Learning (DL). The system is a three-layered architecture with the Sensing layer first, Fog Computing layer in between, and Cloud Computing layer at the end. Fog Computing is a powerful new generation paradigm that brings storage, computation, and networking at the edge of the IoT network and reduce network latency. A DL based BiLSTM (Bidirectional Long Short-Term Memory) model is deployed in the Fog Computing layer. The proposed system aims at real-time monitoring and accurate air quality forecasting to support decision making and aid timely prevention and control of pollutant emissions by alerting the stakeholders when a dangerous Air Quality Index (AQI) is expected. Experimental results show that the BiLSTM model has a better predictive performance considering the meteorological parameters than the baseline models in terms of MAE and RMSE. A proof of concept realizing the proposed system is elaborated in the paper.