Volterra Graph-Based Outlier Detection for Air Pollution Sensor Networks

Ferrer-Cid, Pau; Barceló-Ordinas, José M.; García-Vidal, Jorge

doi:10.1109/tnse.2022.3169220

Cited by 14 publications

(4 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input signal is filtered with a high-pass convolutional filter and the signal is classified as anomalous if one or more GFT coefficients exceed a threshold. The works [209]- [211] consider nonlinear filters (Sec. VI-C) to reconstruct the data under normal behavior and the low-pass signal components are used to detect and localize anomalous sensors.…”

Section: B Anomaly Detectionmentioning

confidence: 99%

Graph Filters for Signal Processing and Machine Learning on Graphs

Isufi¹,

Gama²,

Shuman³

et al. 2022

Preprint

View full text Add to dashboard Cite

Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article serves the dual purpose of providing a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations between signal processing, machine learning, and application domains.

show abstract

Section: B Anomaly Detectionmentioning

confidence: 99%

Graph Filters for Signal Processing and Machine Learning on Graphs

Isufi¹,

Gama²,

Shuman³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Data pre-processing has a big impact on the subsequent representation of the data. As mentioned above, having the data synchronized with reference stations, in the environment where the node will be deployed, allows us to calibrate the sensors and to detect drifts, aging or outliers [ 38 , 39 , 40 ], which will lead to a recalibration of the sensor. Specifically, we divide the pre-processing operation into three stages; the sampling of the sensor, the filtering of the collected samples, and the aggregation of these samples.…”

Section: Sensor Data Gathering Pipelinementioning

confidence: 99%

Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors

Ferrer-Cid

Garcia-Calvete²,

Aina

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.

show abstract

“…One simple approach to include neighboring data to predict or impute air quality data is to consider spatial distances or correlations between the stations. − A more advanced solution to this is graph machine learning, , a subfield of graph signal processing , which allows machine learning on irregularly structured data such as a monitoring network. Graph-based methods have been adopted for air quality-related tasks, such as outlier detection, postprocessing of low-cost sensor data, or high-resolution forecasting. − Graph machine learning was shown to be suitable for the imputation of different data sets, − yet, to the best of our knowledge, they have not yet been used to impute missing air quality data.…”

Section: Introductionmentioning

confidence: 99%

Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data

Betancourt,

Li,

Kleinert

et al. 2023

Environ. Sci. Technol.

View full text Add to dashboard Cite

Gaps in the measurement series of atmospheric pollutants can impede the reliable assessment of their impacts and trends. We propose a new method for missing data imputation of the air pollutant tropospheric ozone by using the graph machine learning algorithm “correct and smooth”. This algorithm uses auxiliary data that characterize the measurement location and, in addition, ozone observations at neighboring sites to improve the imputations of simple statistical and machine learning models. We apply our method to data from 278 stations of the year 2011 of the German Environment Agency (Umweltbundesamt – UBA) monitoring network. The preliminary version of these data exhibits three gap patterns: shorter gaps in the range of hours, longer gaps of up to several months in length, and gaps occurring at multiple stations at once. For short gaps of up to 5 h, linear interpolation is most accurate. Longer gaps at single stations are most effectively imputed by a random forest in connection with the correct and smooth. For longer gaps at multiple stations, the correct and smooth algorithm improved the random forest despite a lack of data in the neighborhood of the missing values. We therefore suggest a hybrid of linear interpolation and graph machine learning for the imputation of tropospheric ozone time series.

show abstract

Volterra Graph-Based Outlier Detection for Air Pollution Sensor Networks

Cited by 14 publications

References 50 publications

Graph Filters for Signal Processing and Machine Learning on Graphs

Graph Filters for Signal Processing and Machine Learning on Graphs

Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors

Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data

Contact Info

Product

Resources

About