ArticleSensors (Basel, Switzerland)2022
IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A Multi-Source Sensor Dataset for Spain: Integrating Air Quality, Meteorological, Mobility and Calendar Records.Sensors (Basel, Switzerland) · 2026Article
- Five Years of Accurate PMInternational journal of environmental research and public health · 2023Article
- Reflection Symmetry Detection in Earth Observation Data.Sensors (Basel, Switzerland) · 2023Article
- Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
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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Registered trials
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