Air Pollution Monitoring Using WSN Nodes with Machine Learning Techniques: A Case Study. Rosero-Montalvo, P., D., López-Batista, V., F., Arciniega-Rocha, R., & Peluffo-Ordóñez, D., H. Logic Journal of the IGPL, 2, 2021. ![Air Pollution Monitoring Using WSN Nodes with Machine Learning Techniques: A Case Study [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 11 downloads 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.
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Downloads: 11
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