| Title: | IoT and satellite sensor data integration for assessment of environmental variables: a case study on NO2 |
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| Authors: | ID Cukjati, Jernej (Author)
ID Mongus, Domen (Author)
ID Rizman Žalik, Krista (Author)
ID Žalik, Borut (Author) |
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| Files: |  IoT_and_Satellite_Sensor_Data_Int-Cukjati-2022.pdf (3,72 MB)
MD5: 42F71FB273A6D70746CFBA9474CBC28B
 https://www.mdpi.com/1424-8220/22/15/5660
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO2 data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an RMSE of 15.49 ×10−6 mol/m2. |
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| Keywords: | Internet of Things, IoT, remote sensing, sensor integration, machine learning, ensemble method |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 19.05.2022 |
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| Article acceptance date: | 25.07.2022 |
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| Publication date: | 28.07.2022 |
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| Publisher: | MDPI |
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| Year of publishing: | 2022 |
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| Number of pages: | 16 str. |
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| Numbering: | Letn. 22, Št. 15, št. članka 5660 |
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| PID: | 20.500.12556/DKUM-85976  |
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| UDC: | 004.9 |
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| ISSN on article: | 1424-8220 |
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| COBISS.SI-ID: | 118032387 |
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| DOI: | 10.3390/s22155660 |
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| Copyright: | © 2022 by the authors |
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| Publication date in DKUM: | 22.09.2023 |
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| Views: | 708 |
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| Downloads: | 177 |
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| Metadata: |    |
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| Categories: | Misc.
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