Arquitectura computacional de analítica de datos IoT para el connected home basada en deep learning

dc.contributorIsaza Echeverri, Gustavo Adolfo
dc.contributorCastillo Ossa, Luis Fernando
dc.creatorCastañeda Osorio, Carlos Andres
dc.date2021-10-21T22:05:32Z
dc.date2021-10-21T22:05:32Z
dc.date2021-10-10
dc.date.accessioned2023-09-06T18:17:11Z
dc.date.available2023-09-06T18:17:11Z
dc.identifierhttps://repositorio.ucaldas.edu.co/handle/ucaldas/17178
dc.identifierUniversidad de Caldas
dc.identifierRepositorio institucional Universidad de Caldas
dc.identifierhttps://repositorio.ucaldas.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8694756
dc.descriptionIlustraciones, gráficas
dc.descriptionspa:El internet de las cosas (IoT) es un paradigma informático que se expande día a día junto con la cantidad de dispositivos conectados a la red, por eso transmitir información de manera segura y poder utilizar toda la capacidad computacional de los dispositivos que la componen para analizar los generados. Los datos constituyen uno de los grandes desafíos que se intenta resolver bajo la arquitectura computacional propuesta en el presente artículo.
dc.descriptioneng:The internet of things (IoT) is a computing paradigm that expands every day along with the number of devices connected to the network, that’s why transmit information safely and be able to use all the computational capacity of the devices that compose it to analyze the generated data is one of the great challenges that it is tried to solve under the computational architecture proposed in the present article.
dc.description1. Introducción / 1.1. Campo Temático /1.2. Planteamiento del Problema . . . . . . / 1.3. Justificación / 1.4. Objetivos /1.4.1. Objetivo General/ 1.4.2. Objetivos Específicos / 1.5. Estructura del documento / 2. Revisión Bibliográfica /´ 2.1. IoT / 2.2. MQTT / 2.3. Machine Learning / 2.3.1. Máquinas de soporte vectorial / 2.3.2. Clustering con K-Means / 2.3.3. Deep Learning/ 2.3.4. Redes neuronales convolucionales (ConvNets) / 2.3.5. Redes neuronales / 2.4. Big Data . . . . . . . . / 2.5. Fog Computing / 3. Descripción detallada del proceso / 3.1. Materiales y métodos / 3.1.1. Benchmarking a proveedores de servicios en la nube / 3.1.2. Distribución de los Datos analizados / 3.1.3. Dispositivos Físicos Utilizados/ 3.1.4. Servicios nube / 3.1.5. Aplicaciones de algoritmos de Deep learning para el hogar conectado / 3.1.6. Diseño de la solución ˜ on /3.1.7. Detalles de aplicación implementación ´ on y validación ´ / 3.1.8. Resumen / 4 4. Análisis de Resultados / 4.1. Resultados del Fog computing / 4.2. Fog Computing con Movidious / 4.3. Resultados de transmisión y almacenamiento en la nube/ 4.4. Resultados prueba de validación con datos de cocina / 4.5. Resultados del modelo de redes neuronales convolucionales para el análisis de imágenes /4.6. Resumen / 5. Conclusiones y trabajo futuro
dc.descriptionMaestría
dc.descriptionMagister en Ingeniería Computacional
dc.descriptionMachine learning
dc.descriptionInternet of Things
dc.descriptionInteligencia Artificial
dc.formatapplication/pdf
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dc.formatapplication/pdf
dc.languageeng
dc.languagespa
dc.publisherFacultad de Ingeniería
dc.publisherManizales
dc.publisherMaestría en Ingeniería Computacional
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dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDatos
dc.subjectProceso electrónico de datos
dc.subjectSoftware
dc.subjectInternet of Things
dc.subjectMachine learning
dc.subjectFog Computing
dc.subjectdeep learning
dc.subjectAnalítica de datos
dc.titleArquitectura computacional de analítica de datos IoT para el connected home basada en deep learning
dc.typeTrabajo de grado - Maestría
dc.typehttp://purl.org/coar/resource_type/c_bdcc
dc.typeText
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typehttps://purl.org/redcol/resource_type/TM
dc.typeinfo:eu-repo/semantics/publishedVersion


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