Research Article
Air Quality Monitor and Forecast in Norway Using NB-IoT and Machine Learning
@INPROCEEDINGS{10.1007/978-3-030-51005-3_7, author={Andreas Lepper\`{u}d and Hai Thanh Nguyen and Sigmund Akselsen and Leendert Wienhofen and Pinar \`{U}zturk and Weiqing Zhang}, title={Air Quality Monitor and Forecast in Norway Using NB-IoT and Machine Learning}, proceedings={Science and Technologies for Smart Cities. 5th EAI International Summit, SmartCity360, Braga, Portugal, December 4-6, 2019, Proceedings}, proceedings_a={SMARTCITY}, year={2020}, month={7}, keywords={Air quality Internet of Things Machine learning}, doi={10.1007/978-3-030-51005-3_7} }- Andreas Lepperød
Hai Thanh Nguyen
Sigmund Akselsen
Leendert Wienhofen
Pinar Øzturk
Weiqing Zhang
Year: 2020
Air Quality Monitor and Forecast in Norway Using NB-IoT and Machine Learning
SMARTCITY
Springer
DOI: 10.1007/978-3-030-51005-3_7
Abstract
In recent years, air quality has become a significant environmental and health related issue due to rapid urbanization and industrialization. As a consequence, real-time monitoring and precise prediction of air quality gained increased importance. In this paper, we present a complete solution to this problem by using NB-IoT (Narrowband-Internet-of-Things) sensors and machine learning techniques. This solution includes our own compiled cheap micro-sensor devices that are planned to be deployed at stationary locations as well as on the moving vehicles to provide a comprehensive overview of air quality in the city. We developed our own IoT data and analysis platform to support the gathering of air quality data as well as weather and traffic data from external sources. We applied seven machine learning methods to predict air quality in the next 48-h, which showed promising results. Finally, we developed a mobile application named Lufta, which is now available in Google play for testing purposes.
