DOI:
https://doi.org/10.14483/23448393.19667Published:
2023-02-28Issue:
Vol. 28 No. Suppl (2023): Bogotá, Committed with the Development of Science and TechnologySection:
Civil and Environmental EngineeringTransient DynamicWeather Isolines Generated via IoT Temperature and Relative Humidity Analysis Using the NodeMCU ESP8266 in Bogotá
Isolíneas meteorológicas dinámicas transitorias generadas a partir del análisis IoT de temperatura y humedad relativa utilizando el NodeMCU ESP8266 en Bogotá
Keywords:
®Arduino, Internet of Things, Isotherms, ®MATLAB, ®ThingSpeak, NodeMCU ESP8266, Uncertainty, DHT11 (en).Keywords:
®Arduino, Incertidumbre, Internet de las cosas, Isotermas, ®MATLAB, DHT11, ®ThingSpeak, NodeMCU ESP8266 (es).Downloads
Abstract (en)
Context: This article presents the real-time estimation of temperature (°C) and relative humidity ( %) (interval of 16 seconds) for the city of Bogota DC via the Internet of Things (IoT).
Method: This prototype is based on the Arduino ESP8266 NodeMCU module and the DHT11 sensor, as well as on a server-client HTPP communication protocol viaWi-Fi, with remote access to information. 16 sensors were installed in Bogota DC. These sensors send the observed data to the MATLAB storage cloud (ThingSpeak) via theWi-Fi module and can be downloaded in real-time. The temperature (°C) and relative humidity ( %) values were calibrated based on measurements made by the TTH002-certified digital thermo-hygrometer.
Results: Based on the average temperature and relative humidity obtained, two maps were elaborated by implementing QGis: one with the isotherms and another one with isohumes. The inverse distance weighting (IDW) interpolation algorithm was used.
Conclusions: The use of monitoring devices based on the IoT significantly contributes to automating meteorological data and structuring and utilizing robust databases in the field of Civil Engineering. Thus, the real-time transmission of temperature and relative humidity data allows for the online analysis of variables. Finally, the term adaptive dynamic cartography is proposed, which is associated with the generation of maps via the IoT, through which changes in the observed variables are displayed in real time, which allows monitoring the variables making adjustments based on an interpolation algorithm, as well as automatically and instantaneously generating isolines, which significantly reduces the uncertainty implied by the spatial-temporal resolution of current cartography.
Abstract (es)
Contexto: Este articulo presenta la estimación de la temperatura (°C) y la humedad relativa ( %) en tiempo real (intervalo de 4 segundos) para la ciudad de Bogotá DC a través del Internet de las Cosas (IoT).
Metodo: Este prototipo se basa en el módulo NodeMCU ESP8266 de Arduino y el sensor DHT11, así como en un protocolo servidor-cliente de comunicación HTPP vía Wifi, con acceso remoto a la informacion. Se instalaron 16 sensores en Bogotá DC. Estos sensores envían los datos observados a la nube de almacenamiento de MATLAB (ThingSpeak) a través del módulo Wifi y pueden ser descargados en tiempo real. Los valores de temperatura (°C) y humedad relativa ( %) fueron calibrados a partir de mediciones realizadas por el termohigrómetro digital certificado TTH002.
Resultados: A partir de la temperatura medias y la humedad relativa obtenidas, se realizaron dos mapas implementando QGis: uno de isotermas y otro de isohumas. Se utilizo el algoritmo de interpolación por ponderación de distancia inversa (IDW).
Conclusiones: La utilización de dispositivos de monitoreo a partir del Internet de las cosas (IoT) contribuye de manera significativa a la automatización de datos meteorológicos y al uso y estructuración de bases de datos robustas en el campo de la Ingeniería Civil. Así, la transmisión en tiempo real de los datos de temperatura y humedad relativa permiten analizar variables en línea. Finalmente, se propone el termino cartografía dinámica adaptativa, asociado a la generación de mapas a partir del IoT, mediante el cual se visualizan cambios en la variable observada en tiempo real, lo que permite monitorear la variable y hacer ajustes a partir de un algoritmo de interpolación, así como la generación automática e instantánea de isolíneas, lo cual reduce de forma significativa la incertidumbre existente en la resolución temporal y espacial de la cartografía actual.
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