Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Air Quality Sensor Fusion

• URL: http://arxiv.org/abs/2411.06917v1
• Date: Mon, 11 Nov 2024 12:20:57 GMT
• Title: Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Air Quality Sensor Fusion
• Authors: Keivan Faghih Niresi, Ismail Nejjar, Olga Fink,
• Abstract summary: We propose a novel unsupervised domain adaptation (UDA) method specifically tailored for regression tasks on graph-structured data. We incorporate spatial-temporal graph neural networks (STGNNs) to model the relationships between sensors. Our approach allows low-cost IoT sensors to learn calibration parameters from expensive reference sensors.
• Score: 6.963971634605796
• License:
• Abstract: The deployment of affordable Internet of Things (IoT) sensors for air pollution monitoring has increased in recent years due to their scalability and cost-effectiveness. However, accurately calibrating these sensors in uncontrolled environments remains a significant challenge. While expensive reference sensors can provide accurate ground truth data, they are often deployed on a limited scale due to high costs, leading to a scarcity of labeled data. In diverse urban environments, data distributions constantly shift due to varying factors such as traffic patterns, industrial activities, and weather conditions, which impact sensor readings. Consequently, traditional machine learning models -- despite their increasing deployment for environmental sensor calibration -- often struggle to provide reliable pollutant measurements across different locations due to domain shifts. To address these challenges, we propose a novel unsupervised domain adaptation (UDA) method specifically tailored for regression tasks on graph-structured data. Our approach leverages Graph Neural Networks (GNNs) to model the relationships between sensors. To effectively capture critical spatial-temporal interactions, we incorporate spatial-temporal graph neural networks (STGNNs), which extend GNNs by incorporating temporal dynamics. To handle the resulting larger embeddings, we propose a domain adaptation method using a closed-form solution inspired by the Tikhonov-regularized least-squares problem. This method leverages Cholesky decomposition and power iteration to align the subspaces between source and target domains. By aligning these subspaces, our approach allows low-cost IoT sensors to learn calibration parameters from expensive reference sensors. This facilitates reliable pollutant measurements in new locations without the need for additional costly equipment.

Related papers

• DiffuBox: Refining 3D Object Detection with Point Diffusion [74.01759893280774]
  We introduce a novel diffusion-based box refinement approach to ensure robust 3D object detection and localization. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets.
  arXiv  Detail & Related papers  (2024-05-25T03:14:55Z)
• Sensor Placement for Learning in Flow Networks [6.680930089714339]
  This paper investigates the sensor placement problem for networks. We first formalize the problem under a flow conservation assumption and show that it is NP-hard to place a fixed set of sensors optimally. Next, we propose an efficient and adaptive greedy for sensor placement that scales to large networks.
  arXiv  Detail & Related papers  (2023-12-12T01:08:08Z)
• Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems [8.997596859735516]
  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.
  arXiv  Detail & Related papers  (2023-09-08T12:04:47Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems [4.193524211159057]
  In this paper, we analyze data from sensors deployed in an agricultural farm with data from seven different kinds of sensors, and from an advanced manufacturing testbed with vibration sensors. We show how in these two application domains, predictive failure classification can be achieved, thus paving the way for predictive maintenance.
  arXiv  Detail & Related papers  (2021-02-11T02:37:27Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Learning Camera Miscalibration Detection [83.38916296044394]
  This paper focuses on a data-driven approach to learn the detection of miscalibration in vision sensors, specifically RGB cameras. Our contributions include a proposed miscalibration metric for RGB cameras and a novel semi-synthetic dataset generation pipeline based on this metric. By training a deep convolutional neural network, we demonstrate the effectiveness of our pipeline to identify whether a recalibration of the camera's intrinsic parameters is required or not.
  arXiv  Detail & Related papers  (2020-05-24T10:32:49Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.