Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air
  Pollution Monitoring Systems
Keivan Faghih Niresi Mengjie Zhao Hugo Bissig Henri Baumann Olga Fink
The use of Internet of Things (IoT) sensors for air pollution monitoring has
significantly increased, resulting in the deployment of low-cost sensors.
Despite this advancement, accurately calibrating these sensors in uncontrolled
environmental conditions remains a challenge. To address this, we propose a
novel approach that leverages graph neural networks, specifically the graph
attention network module, to enhance the calibration process by fusing data
from sensor arrays. Through our experiments, we demonstrate the effectiveness
of our approach in significantly improving the calibration accuracy of sensors
in IoT air pollution monitoring platforms.