Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

• URL: http://arxiv.org/abs/2309.04508v1
• Date: Fri, 8 Sep 2023 12:04:47 GMT
• Title: Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems
• Authors: Keivan Faghih Niresi, Mengjie Zhao, Hugo Bissig, Henri Baumann, and Olga Fink
• Abstract summary: We propose a novel approach to enhance the calibration process by fusing data from sensor arrays. We demonstrate the effectiveness of our approach in significantly improving the calibration accuracy of sensors in IoT air pollution monitoring platforms.
• Score: 8.997596859735516
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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