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Please use this identifier to cite or link to this item:
https://repositorio.utn.edu.ec/handle/123456789/19032Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Alvear-Puertas, Vanessa | - |
| dc.contributor.author | Rosero-Montalvo, Paul D | - |
| dc.contributor.author | Michilena Calderón, Jaime R | - |
| dc.contributor.author | Arciniega-Rocha, Ricardo P | - |
| dc.contributor.author | Erazo-Chamorro, Vanessa | - |
| dc.date.accessioned | 2026-02-26T23:30:21Z | - |
| dc.date.available | 2026-02-26T23:30:21Z | - |
| dc.date.created | 2020-11-01 | - |
| dc.date.issued | 2026-02-26 | - |
| dc.identifier.issn | 2194-5357 | - |
| dc.identifier.uri | https://repositorio.utn.edu.ec/handle/123456789/19032 | - |
| dc.description.abstract | El incremento de las emisiones de contaminantes atmosféricos constituye una preocupación actual. Debido a ello, el presente trabajo presenta una red de nodos sensores que envían información mediante el protocolo LoRa para el monitoreo de emisiones de gases nocivos para la salud en entornos urbanos. Para ello, se propone un esquema electrónico para la adquisición de datos que incluye un proceso de suavizado de la señal de cada sensor con el fin de eliminar el ruido. Posteriormente, se realiza el análisis de los datos utilizando una red neuronal artificial, cuyo objetivo principal es clasificar el estado de la calidad del aire. Como resultados relevantes, se obtiene un desempeño de clasificación del 95 % en pruebas controladas y del 90 % en condiciones reales, además de la presentación de esta información en tiempo real. | es_EC |
| dc.language.iso | spa | es_EC |
| dc.publisher | Springer Nature Link | es_EC |
| dc.rights | openAccess | es_EC |
| dc.rights | Atribución-NoComercial-CompartirIgual 3.0 Ecuador | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/ec/ | * |
| dc.subject | INFORMACIÓN | es_EC |
| dc.subject | ANÁLISIS | es_EC |
| dc.subject | DESEMPEÑO | es_EC |
| dc.title | Monitoreo de la contaminación del aire urbano mediante redes neuronales y redes de sensores inalámbricos basadas en LoRa | es_EC |
| dc.type | Article | es_EC |
| dc.description.degree | N/A | es_EC |
| dc.coverage | Ibarra-Ecuador | es_EC |
| dc.contributor.orcid | https://orcid.org/0000-0003-2332-6073 | es_EC |
| dc.contributor.orcid | https://orcid.org/0000-0003-1995-400X | es_EC |
| dc.contributor.orcid | https://orcid.org/0000-0002-8819-8167 | es_EC |
| dc.contributor.orcid | https://orcid.org/0000-0001-6960-1718 | es_EC |
| dc.contributor.orcid | https://orcid.org/0000-0003-0732-0384 | es_EC |
| dc.title.en | Urban air pollution monitoring by neural networks and wireless sensor networks based on LoRa | es_EC |
| dc.subject.en | INFORMATION | es_EC |
| dc.subject.en | ANALYSIS | es_EC |
| dc.subject.en | PERFORMANCE | es_EC |
| dc.description.abstract-en | The increase in air pollutant emissions is a current concern. Due to this, the present work shows a network of sensor nodes sending information by LoRa protocol to the monitoring of emissions of harmful gases for health in urban environments. To do this, an electronic scheme is proposed for data acquisition with a smoothing of the signal from each sensor for noise elimination. Subsequently, data analysis is performed using an artificial neural network with the main objective of classifying the state of the air. As relevant results, the classification performance of 95% in tests and 90% in real conditions with the presentation of this information in real-time is obtained. | es_EC |
| dc.identifier.doi | https://link.springer.com/chapter/10.1007/978-3-030-63089-8_59 | es_EC |
| Appears in Collections: | Artículos | |
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