-
Views
-
Cite
Cite
Paul D Rosero-Montalvo, Vivian F López-Batista, Ricardo Arciniega-Rocha, Diego H Peluffo-Ordóñez, Air Pollution Monitoring Using WSN Nodes with Machine Learning Techniques: A Case Study, Logic Journal of the IGPL, Volume 30, Issue 4, August 2022, Pages 599–610, https://doi.org/10.1093/jigpal/jzab005
- Share Icon Share
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.