Name
Title | ||
|---|---|---|
Air Pollution Monitoring Using WSN Nodes with Machine Learning Techniques: A Case Study |
Abstract | ||
|---|---|---|
Air pollution is a current concern of people and government entities. Therefore, in urban scenarios, its monitoring and subsequent analysis is a remarkable and challenging issue due mainly to the variability of polluting-related factors. For this reason, the present work shows the development of a wireless sensor network that, through machine learning techniques, can be classified into three different types of environments: high pollution levels, medium pollution and no noticeable contamination into the Ibarra City. To achieve this goal, signal smoothing stages, prototype selection, feature analysis and a comparison of classification algorithms are performed. As relevant results, there is a classification performance of 95% with a significant noisy data reduction. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/jigpal/jzab005 | LOGIC JOURNAL OF THE IGPL |
Keywords | DocType | Volume |
WSN, air pollution, data analysis | Journal | 30 |
Issue | ISSN | Citations |
4 | 1367-0751 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul D Rosero-Montalvo | 1 | 1 | 0.69 |
| Vivian F López-Batista | 2 | 0 | 0.34 |
| Ricardo Arciniega-Rocha | 3 | 0 | 0.34 |
| Diego H Peluffo-Ordóñez | 4 | 0 | 0.34 |