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<dim:field authority="0000-0002-0875-2632" element="contributor" qualifier="advisor" confidence="ACCEPTED" language="es-ES" mdschema="dc">Robledo Cabezuela, Raul</dim:field>
<dim:field authority="8222f652-86af-480f-80cc-449d32fef897" element="contributor" qualifier="author" confidence="UNCERTAIN" language="es-ES" mdschema="dc">Junco Miralles, Aurora</dim:field>
<dim:field element="contributor" qualifier="other" language="es_ES" mdschema="dc">Universidad Pontificia Comillas, Escuela Técnica Superior de Ingeniería (ICAI)</dim:field>
<dim:field element="date" qualifier="accessioned" mdschema="dc">2024-10-10T07:31:31Z</dim:field>
<dim:field element="date" qualifier="available" mdschema="dc">2024-10-10T07:31:31Z</dim:field>
<dim:field element="date" qualifier="issued" language="es_ES" mdschema="dc">2025</dim:field>
<dim:field element="identifier" qualifier="uri" mdschema="dc">http://hdl.handle.net/11531/95032</dim:field>
<dim:field element="description" language="es_ES" mdschema="dc">Grado en Ingeniería en Tecnologías de Telecomunicación y Grado en Análisis de Negocios/Business Analytics</dim:field>
<dim:field element="description" qualifier="abstract" language="es-ES" mdschema="dc">Este proyecto busca desarrollar un sistema de optimización de redes de sensores y actuadores en entornos IoT industriales mediante técnicas de Inteligencia Artificial. El objetivo principal es mejorar la eficiencia de la transmisión de datos y optimizar la toma de decisiones en tiempo real, centrándose en los retos que presentan infraestructuras como plantas fotovoltaicas. El sistema combina sensores ADXL345 conectados a microcontroladores ESP32, comunicación mediante LoRaWAN y MQTT, y modelos de Machine Learning para la detección de eventos y la predicción de respuestas de control.
Los resultados obtenidos demuestran las posibles mejoras significativas en la eficiencia energética, precisión en la detección de eventos anómalos y una notable reducción del tráfico de datos transmitido, contribuyendo a redes de sensores más sostenibles e inteligentes.</dim:field>
<dim:field element="description" qualifier="abstract" language="en-GB" mdschema="dc">This project aims to develop a system for optimizing sensor and actuator networks in industrial IoT environments using Artificial Intelligence techniques. The main objective is to improve data transmission efficiency and optimize real-time decision-making, focusing on the challenges presented by infrastructures such as photovoltaic plants. The system combines ADXL345 sensors connected to ESP32 microcontrollers, communication via LoRaWAN and MQTT, and Machine Learning models for event detection and control response prediction. 
The results demonstrate significant potential improvements in energy efficiency, accuracy in detecting anomalous events, and a notable reduction in transmitted data traffic, contributing to more sustainable and intelligent sensor networks.</dim:field>
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<dim:field element="rights" language="es_ES" mdschema="dc">Attribution-NonCommercial-NoDerivs 3.0 United States</dim:field>
<dim:field element="rights" qualifier="uri" language="es_ES" mdschema="dc">http://creativecommons.org/licenses/by-nc-nd/3.0/us/</dim:field>
<dim:field element="subject" qualifier="other" language="es_ES" mdschema="dc">KTT (GITT)</dim:field>
<dim:field element="title" language="es_ES" mdschema="dc">Optimización de Redes de Sensores y Actuadores para Aplicaciones IoT con Inteligencia Artificial.</dim:field>
<dim:field element="type" language="es_ES" mdschema="dc">info:eu-repo/semantics/bachelorThesis</dim:field>
<dim:field element="rights" qualifier="accessRights" language="es_ES" mdschema="dc">info:eu-repo/semantics/openAccess</dim:field>
<dim:field element="keywords" language="es-ES" mdschema="dc">IoT, Inteligencia Artificial, MQTT, LoRaWAN, Machine Learning, Optimización de redes</dim:field>
<dim:field element="keywords" language="en-GB" mdschema="dc">IoT, Artificial Intelligence, MQTT, LoRaWAN, Machine Learning, Network Optimization</dim:field>
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