Sensor Sampling Trade-Offs for Air Quality Monitoring With Low-Cost Sensors

• URL: http://arxiv.org/abs/2112.09072v1
• Date: Tue, 14 Dec 2021 11:05:55 GMT
• Title: Sensor Sampling Trade-Offs for Air Quality Monitoring With Low-Cost Sensors
• Authors: Pau Ferrer-Cid, Julio Garcia-Calvete, Aina Main-Nadal, Zhe Ye, Jose M. Barcelo-Ordinas and Jorge Garcia-Vidal
• Abstract summary: We show the impact of the data sampling strategy in the calibration of tropospheric ozone, nitrogen dioxide and nitrogen monoxide low-cost sensors. Specifically, we show how a sampling strategy that minimizes the duty cycle of the sensing subsystem can reduce power consumption while maintaining data quality.
• Score: 0.1957338076370071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The calibration of low-cost sensors using machine learning techniques is a methodology widely used nowadays. Although many challenges remain to be solved in the deployment of low-cost sensors for air quality monitoring, low-cost sensors have been shown to be useful in conjunction with high-precision instrumentation. Thus, most research is focused on the application of different calibration techniques using machine learning. Nevertheless, the successful application of these models depends on the quality of the data obtained by the sensors, and very little attention has been paid to the whole data gathering process, from sensor sampling and data pre-processing, to the calibration of the sensor itself. In this article, we show the main sensor sampling parameters, with their corresponding impact on the quality of the resulting machine learning-based sensor calibration and their impact on energy consumption, thus showing the existing trade-offs. Finally, the results on an experimental node show the impact of the data sampling strategy in the calibration of tropospheric ozone, nitrogen dioxide and nitrogen monoxide low-cost sensors. Specifically, we show how a sampling strategy that minimizes the duty cycle of the sensing subsystem can reduce power consumption while maintaining data quality.

Related papers

• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• 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)
• Detection of Sensor-To-Sensor Variations using Explainable AI [2.2956649873563952]
  chemi-resistive gas sensing devices are plagued by issues of sensor variations during manufacturing. This study proposes a novel approach for detecting sensor-to-sensor variations in sensing devices using the explainable AI (XAI) method of SHapley Additive exPlanations (SHAP) The methodology is tested using artificial and realistic Ozone concentration profiles to train a Gated Recurrent Unit (GRU) model.
  arXiv  Detail & Related papers  (2023-06-19T11:00:54Z)
• 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)
• Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning [11.352677351165246]
  Low-cost particulate matter sensors are transforming air quality monitoring. These sensors require training data from co-deployed reference monitors. We propose novel transfer learning methods for quick calibration of sensors.
  arXiv  Detail & Related papers  (2021-08-02T05:19:23Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Data-based Design of Inferential Sensors for Petrochemical Industry [0.0]
  Inferential (or soft) sensors are used in industry to infer the values of imprecisely and rarely measured (or completely unmeasured) variables from variables measured online. This work is focused on the design of inferential sensors for product composition of an industrial distillation column in two oil refinery units.
  arXiv  Detail & Related papers  (2021-06-25T08:48:50Z)
• 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)
• Data-aided Sensing for Gaussian Process Regression in IoT Systems [48.523643863141466]
  We use data-aided sensing to learn data sets collected from sensors in Internet-of-Things systems. We show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing.
  arXiv  Detail & Related papers  (2020-11-23T20:59:51Z)
• 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)

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.