Aprendizaje de sensorizado de entornos IoT mediante BeagleBone
- Alejandro Millan del Rio 1
- José Alberto Benítez Andrades 1
- María del Carmen Benavides Cuéllar 1
- Bruno Fernandes 2
- Fábio Silva 3
- José-Luis Casteleiro-Roca 4
- Isaías García Rodríguez 1
- Héctor Alaiz Moretón 1
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1
Universidad de León
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2
Universidade do Minho
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3
Instituto Politécnico do Porto
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4
Universidade da Coruña
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- José Luis Calvo Rolle (coord.)
- José-Luis Casteleiro-Roca (coord.)
- Isabel Fernández-Ibáñez (coord.)
- Óscar Fontenla Romero (coord.)
- Esteban Jove Pérez (coord.)
- Alberto J. Leira-Rejas (coord.)
- José Antonio López Vázquez (coord.)
- Vanesa Loureiro-Vázquez (coord.)
- María-Carmen Meizoso-López (coord.)
- Francisco Javier Pérez Castelo (coord.)
- Andrés José Piñón Pazos (coord.)
- Héctor Quintián Pardo (coord.)
- Juan Manuel Rivas Rodríguez (coord.)
- Benigno Antonio Rodríguez Gómez (coord.)
- Rafael A. Vega-Vega (coord.)
Publisher: Servizo de Publicacións ; Universidad de La Coruña
ISBN: 978-84-9749-716-9
Year of publication: 2019
Pages: 302-308
Congress: Jornadas de Automática (40. 2019. Ferrol)
Type: Conference paper
Abstract
The present work is oriented to the exploitation of the microcomputer called BeagleBone Black, and its use for learning purposes like an alternative to famous Arduino and RaspBerry PI. The work will present two basics implementations in order to demonstrate the feasibility of this solution for sensorized a domestic IoT environment. In addition. a comparison of the various existing solutions in the market is addressed.