mlsanityData to 14 Oct 2026

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

arXiv 2309.04508 ↗First posted 8 Sep 2023cs.LG cs.AI eess.SPcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 8 Sep 2023.

Builds on

10 of 25 references.

References · 10

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  2. 2017Graph Attention Networkscited by 8,923
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  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
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  10. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 146

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Cited by year

Citing papers per year: 2024: 4.

4 papers.

  1. 2024Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamicscited by 7
  2. 2024Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networkscited by 3
  3. 2024Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusioncited by 2
  4. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0