Next Article in Journal
End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance
Next Article in Special Issue
EFAR-MMLA: An Evaluation Framework to Assess and Report Generalizability of Machine Learning Models in MMLA
Previous Article in Journal
Optimization and Validation of a Classification Algorithm for Assessment of Physical Activity in Hospitalized Patients
Previous Article in Special Issue
Epistemic Network Analyses of Economics Students’ Graph Understanding: An Eye-Tracking Study
Article

Implementation of a MEIoT Weather Station with Exogenous Disturbance Input

1
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico
2
Centro de Investigación, Innovación y Desarrollo Tecnológico CIIDETEC-UVM, Universidad del Valle de México, Tlaquepaque 45601, Jalisco, Mexico
3
Department of Mathematics and Physics, ITESO AC, Tlaquepaque 45604, Jalisco, Mexico
*
Author to whom correspondence should be addressed.
Academic Editors: José A. Ruipérez-Valiente, Roberto Martinez-Maldonado, Daniele Di Mitri and Jan Schneider
Sensors 2021, 21(5), 1653; https://doi.org/10.3390/s21051653
Received: 31 January 2021 / Revised: 17 February 2021 / Accepted: 19 February 2021 / Published: 27 February 2021
(This article belongs to the Special Issue From Sensor Data to Educational Insights)
Due to the emergence of the coronavirus disease (COVID 19), education systems in most countries have adapted and quickly changed their teaching strategy to online teaching. This paper presents the design and implementation of a novel Internet of Things (IoT) device, called MEIoT weather station, which incorporates an exogenous disturbance input, within the National Digital Observatory of Smart Environments (OBNiSE) architecture. The exogenous disturbance input involves a wind blower based on a DC brushless motor. It can be controlled, via Node-RED platform, manually through a sliding bar, or automatically via different predefined profile functions, modifying the wind speed and the wind vane sensor variables. An application to Engineering Education is presented with a case study that includes the instructional design for the least-squares regression topic for linear, quadratic, and cubic approximations within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work’s main contribution to the state-of-the-art is to turn a weather monitoring system into a hybrid hands-on learning approach thanks to the integrated exogenous disturbance input. View Full-Text
Keywords: sensing system; internet of things; educational mechatronics; engineering education; hands-on learning sensing system; internet of things; educational mechatronics; engineering education; hands-on learning
Show Figures

Figure 1

MDPI and ACS Style

Guerrero-Osuna, H.A.; Luque-Vega, L.F.; Carlos-Mancilla, M.A.; Ornelas-Vargas, G.; Castañeda-Miranda, V.H.; Carrasco-Navarro, R. Implementation of a MEIoT Weather Station with Exogenous Disturbance Input. Sensors 2021, 21, 1653. https://doi.org/10.3390/s21051653

AMA Style

Guerrero-Osuna HA, Luque-Vega LF, Carlos-Mancilla MA, Ornelas-Vargas G, Castañeda-Miranda VH, Carrasco-Navarro R. Implementation of a MEIoT Weather Station with Exogenous Disturbance Input. Sensors. 2021; 21(5):1653. https://doi.org/10.3390/s21051653

Chicago/Turabian Style

Guerrero-Osuna, Héctor A., Luis F. Luque-Vega, Miriam A. Carlos-Mancilla, Gerardo Ornelas-Vargas, Víctor H. Castañeda-Miranda, and Rocío Carrasco-Navarro. 2021. "Implementation of a MEIoT Weather Station with Exogenous Disturbance Input" Sensors 21, no. 5: 1653. https://doi.org/10.3390/s21051653

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop