Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors

Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices and communication technologies to collect a large amount of data for various applications. Smart cities employ IoT infrastructures to build services useful for the administration of the city and the citizen...

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Bibliographic Details
Published in:IEEE internet of things journal 2020-09, Vol.7 (9), p.8943-8955
Main Authors: Do, Tien Huu, Tsiligianni, Evaggelia, Qin, Xuening, Hofman, Jelle, La Manna, Valerio Panzica, Philips, Wilfried, Deligiannis, Nikos
Format: Article
Language:English
Subjects:
Air pollution
Air quality
Atmospheric measurements
Atmospheric modeling
Deep learning
Electronic devices
Internet of Things
Internet of Things (IoT)
Outdoor air quality
Pollutants
Pollution measurement
Sensors
smart cities
Spatial resolution
Stations
Temporal resolution
variational graph autoencoder (VGAE)
Visualization
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Summary:Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices and communication technologies to collect a large amount of data for various applications. Smart cities employ IoT infrastructures to build services useful for the administration of the city and the citizens. In this article, we present an IoT pipeline for acquisition, processing, and visualization of air pollution data over the city of Antwerp, Belgium. Our system employs IoT devices mounted on vehicles as well as static reference stations to measure a variety of city parameters, such as humidity, temperature, and air pollution. Mobile measurements cover a larger area compared to static stations; however, there is a tradeoff between temporal and spatial resolution. We address this problem as a matrix completion on graphs problem and rely on variational graph autoencoders to propose a deep learning solution for the estimation of the unknown air pollution values. Our model is extended to capture the correlation among different air pollutants, leading to improved estimation. We conduct experiments at different spatial and temporal resolution and compare with state-of-the-art methods to show the efficiency of our approach. The observed and estimated air pollution values can be accessed by interested users through a Web visualization tool designed to provide an air pollution map of the city of Antwerp.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.2999446