{"id":{"repo_id":"cadiz","oai_identifier":"oai:rodin.uca.es:10498/39800"},"canonical_url":"https://search.dev.ndltd.org/etd/cadiz/oai:rodin.uca.es:10498/39800","repository":{"repo_id":"cadiz","name":"Universidad de Cadiz","base_url":"https://rodin.uca.es/oai/request"},"display":{"title":"Análisis en Tiempo Real para el Internet Industrial de las Cosas","abstract":"The rapid expansion of the Industrial Internet of Things (IIoT) has led to the largescale deployment of interconnected sensors, machines, and cyber-physical systems capable of generating continuous streams of heterogeneous data. While this transformation enables intelligent automation, predictive maintenance, and data-driven optimization, traditional centralized cloud-based architectures are unable to satisfy the strict latency, scalability, and contextual-awareness requirements of modern Industry 4.0 environments. Addressing these challenges requires distributed, adaptive, and real-time data processing mechanisms capable of operating across multiple computational layers. This doctoral research proposes a context- and situational-aware collaborative architecture for real-time heterogeneous data processing in IIoT ecosystems. The primary contribution is the design and implementation of Atmosphere, a unified edge–fog–cloud framework that integrates multi-agent systems, Complex Event Processing (CEP), and Event-Driven Service-Oriented Architecture (ED-SOA). In the edge layer, autonomous software agents perform local filtering, rule-based reasoning, and immediate decision-making, reducing communication overhead and latency. The fog layer incorporates MQTT-based messaging and CEP engines to aggregate, correlate, and analyze distributed event streams in near real-time, enabling coordinated responses among heterogeneous nodes. The cloud layer provides large-scale analytics, long-term knowledge extraction, global orchestration, and strategic optimization. The architecture ensures bidirectional communication, interoperability, and dynamic adaptation based on contextual information. The second contribution validates the proposed architecture through the design and development of an IoT-based, solar-powered camouflaged robotic system inspired by the Fennec desert fox. The robotic platform operates as an intelligent edge node capable of autonomous data acquisition, context-aware decision-making, and energy-efficient operation in harsh and isolated environments. Experimental evaluations demonstrate improved responsiveness, scalability, and operational efficiency compared to traditional centralized monitoring approaches. Overall, this research advances the state of the art in real-time IIoT analytics by delivering a scalable, distributed, and context-aware framework that bridges heterogeneous data sources, intelligent coordination mechanisms, and practical deployment. The proposed solutions contribute to the realization of resilient, adaptive, and intelligent industrial ecosystems aligned with the principles of Industry 4.0.","abstract_html":"The rapid expansion of the Industrial Internet of Things (IIoT) has led to the largescale deployment of interconnected sensors, machines, and cyber-physical systems capable of generating continuous streams of heterogeneous data. While this transformation enables intelligent automation, predictive maintenance, and data-driven optimization, traditional centralized cloud-based architectures are unable to satisfy the strict latency, scalability, and contextual-awareness requirements of modern Industry 4.0 environments. Addressing these challenges requires distributed, adaptive, and real-time data processing mechanisms capable of operating across multiple computational layers. This doctoral research proposes a context- and situational-aware collaborative architecture for real-time heterogeneous data processing in IIoT ecosystems. The primary contribution is the design and implementation of Atmosphere, a unified edge–fog–cloud framework that integrates multi-agent systems, Complex Event Processing (CEP), and Event-Driven Service-Oriented Architecture (ED-SOA). In the edge layer, autonomous software agents perform local filtering, rule-based reasoning, and immediate decision-making, reducing communication overhead and latency. The fog layer incorporates MQTT-based messaging and CEP engines to aggregate, correlate, and analyze distributed event streams in near real-time, enabling coordinated responses among heterogeneous nodes. The cloud layer provides large-scale analytics, long-term knowledge extraction, global orchestration, and strategic optimization. The architecture ensures bidirectional communication, interoperability, and dynamic adaptation based on contextual information. The second contribution validates the proposed architecture through the design and development of an IoT-based, solar-powered camouflaged robotic system inspired by the Fennec desert fox. The robotic platform operates as an intelligent edge node capable of autonomous data acquisition, context-aware decision-making, and energy-efficient operation in harsh and isolated environments. Experimental evaluations demonstrate improved responsiveness, scalability, and operational efficiency compared to traditional centralized monitoring approaches. Overall, this research advances the state of the art in real-time IIoT analytics by delivering a scalable, distributed, and context-aware framework that bridges heterogeneous data sources, intelligent coordination mechanisms, and practical deployment. The proposed solutions contribute to the realization of resilient, adaptive, and intelligent industrial ecosystems aligned with the principles of Industry 4.0.","abstract_has_math":false,"creators":["Zouai, Meftah"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Ortiz Bellot, Guadalupe","Kazar, Okba"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T01:29:38Z","subjects":[],"languages":["eng"],"rights":["Attribution-NonCommercial-NoDerivatives 4.0 Internacional"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10498/39800","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ortiz Bellot, Guadalupe","Kazar, Okba"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Ingeniería Informática"]},{"key":"dc:creator","label":"Author","values":["Zouai, Meftah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-18T09:45:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-06-18T09:45:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["doctoral thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Attribution-NonCommercial-NoDerivatives 4.0 Internacional"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10498/39800"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The rapid expansion of the Industrial Internet of Things (IIoT) has led to the largescale deployment of interconnected sensors, machines, and cyber-physical systems capable of generating continuous streams of heterogeneous data. While this transformation enables intelligent automation, predictive maintenance, and data-driven optimization, traditional centralized cloud-based architectures are unable to satisfy the strict latency, scalability, and contextual-awareness requirements of modern Industry 4.0 environments. Addressing these challenges requires distributed, adaptive, and real-time data processing mechanisms capable of operating across multiple computational layers. This doctoral research proposes a context- and situational-aware collaborative architecture for real-time heterogeneous data processing in IIoT ecosystems. The primary contribution is the design and implementation of Atmosphere, a unified edge–fog–cloud framework that integrates multi-agent systems, Complex Event Processing (CEP), and Event-Driven Service-Oriented Architecture (ED-SOA). In the edge layer, autonomous software agents perform local filtering, rule-based reasoning, and immediate decision-making, reducing communication overhead and latency. The fog layer incorporates MQTT-based messaging and CEP engines to aggregate, correlate, and analyze distributed event streams in near real-time, enabling coordinated responses among heterogeneous nodes. The cloud layer provides large-scale analytics, long-term knowledge extraction, global orchestration, and strategic optimization. The architecture ensures bidirectional communication, interoperability, and dynamic adaptation based on contextual information. The second contribution validates the proposed architecture through the design and development of an IoT-based, solar-powered camouflaged robotic system inspired by the Fennec desert fox. The robotic platform operates as an intelligent edge node capable of autonomous data acquisition, context-aware decision-making, and energy-efficient operation in harsh and isolated environments. Experimental evaluations demonstrate improved responsiveness, scalability, and operational efficiency compared to traditional centralized monitoring approaches. Overall, this research advances the state of the art in real-time IIoT analytics by delivering a scalable, distributed, and context-aware framework that bridges heterogeneous data sources, intelligent coordination mechanisms, and practical deployment. The proposed solutions contribute to the realization of resilient, adaptive, and intelligent industrial ecosystems aligned with the principles of Industry 4.0.","La rápida expansión del Internet Industrial de las Cosas (IIoT) ha impulsado el despliegue masivo de sensores, máquinas y sistemas ciberfísicos interconectados capaces de generar flujos continuos de datos heterogéneos. Aunque esta transformación permite la automatización inteligente, el mantenimiento predictivo y la optimización basada en datos, las arquitecturas tradicionales centralizadas en la nube no satisfacen los estrictos requisitos de baja latencia, escalabilidad y conciencia contextual exigidos por los entornos modernos de Industria 4.0. Afrontar estos desafíos requiere mecanismos de procesamiento distribuido, adaptativo y en tiempo real que operen a través de múltiples capas computacionales. Esta investigación doctoral propone una arquitectura colaborativa, consciente del contexto y de la situación, para el procesamiento heterogéneo de datos en tiempo real en ecosistemas IIoT. La principal contribución es el diseño e implementación de Atmosphere, un marco unificado edge–fog–cloud que integra sistemas multiagente, Procesamiento de Eventos Complejos (CEP) y Arquitectura Orientada a Servicios basada en Eventos (ED-SOA). En la capa edge, agentes software autónomos realizan filtrado local, razonamiento basado en reglas y toma de decisiones inmediata, reduciendo la sobrecarga de comunicación y la latencia. La capa fog incorpora mensajería basada en MQTT y motores CEP para agregar, correlacionar y analizar flujos de eventos distribuidos en tiempo casi real, permitiendo respuestas coordinadas entre nodos heterogéneos. La capa cloud proporciona analítica a gran escala, extracción de conocimiento a largo plazo, orquestación global y optimización estratégica. La arquitectura garantiza comunicación bidireccional, interoperabilidad y adaptación dinámica basada en información contextual. La segunda contribución valida la arquitectura propuesta mediante el diseño y desarrollo de un sistema robótico camuflado, alimentado por energía solar y basado en IoT, inspirado en el zorro del desierto Fennec. La plataforma robótica actúa como un nodo inteligente en la capa edge, capaz de adquisición autónoma de datos, toma de decisiones consciente del contexto y operación energéticamente eficiente en entornos hostiles y aislados. Las evaluaciones experimentales demuestran mejoras en capacidad de respuesta, escalabilidad y eficiencia operativa en comparación con enfoques tradicionales de monitorización centralizada. En conjunto, esta investigación avanza el estado del arte en analítica en tiempo real para IIoT al ofrecer un marco escalable, distribuido y consciente del contexto que integra fuentes de datos heterogéneas, mecanismos de coordinación inteligente y validación práctica. Las soluciones propuestas contribuyen al desarrollo de ecosistemas industriales resilientes, adaptativos e inteligentes alineados con los principios de la Industria 4.0."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Análisis en Tiempo Real para el Internet Industrial de las Cosas"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ortiz Bellot, Guadalupe","Kazar, Okba"],"dc:contributor.other":["Ingeniería Informática"],"dc:creator":["Zouai, Meftah"],"dc:date.accessioned":["2026-06-18T09:45:47Z"],"dc:date.available":["2026-06-18T09:45:47Z"],"dc:date.issued":["2026"],"dc:description.abstract":["The rapid expansion of the Industrial Internet of Things (IIoT) has led to the largescale deployment of interconnected sensors, machines, and cyber-physical systems capable of generating continuous streams of heterogeneous data. While this transformation enables intelligent automation, predictive maintenance, and data-driven optimization, traditional centralized cloud-based architectures are unable to satisfy the strict latency, scalability, and contextual-awareness requirements of modern Industry 4.0 environments. Addressing these challenges requires distributed, adaptive, and real-time data processing mechanisms capable of operating across multiple computational layers. This doctoral research proposes a context- and situational-aware collaborative architecture for real-time heterogeneous data processing in IIoT ecosystems. The primary contribution is the design and implementation of Atmosphere, a unified edge–fog–cloud framework that integrates multi-agent systems, Complex Event Processing (CEP), and Event-Driven Service-Oriented Architecture (ED-SOA). In the edge layer, autonomous software agents perform local filtering, rule-based reasoning, and immediate decision-making, reducing communication overhead and latency. The fog layer incorporates MQTT-based messaging and CEP engines to aggregate, correlate, and analyze distributed event streams in near real-time, enabling coordinated responses among heterogeneous nodes. The cloud layer provides large-scale analytics, long-term knowledge extraction, global orchestration, and strategic optimization. The architecture ensures bidirectional communication, interoperability, and dynamic adaptation based on contextual information. The second contribution validates the proposed architecture through the design and development of an IoT-based, solar-powered camouflaged robotic system inspired by the Fennec desert fox. The robotic platform operates as an intelligent edge node capable of autonomous data acquisition, context-aware decision-making, and energy-efficient operation in harsh and isolated environments. Experimental evaluations demonstrate improved responsiveness, scalability, and operational efficiency compared to traditional centralized monitoring approaches. Overall, this research advances the state of the art in real-time IIoT analytics by delivering a scalable, distributed, and context-aware framework that bridges heterogeneous data sources, intelligent coordination mechanisms, and practical deployment. The proposed solutions contribute to the realization of resilient, adaptive, and intelligent industrial ecosystems aligned with the principles of Industry 4.0.","La rápida expansión del Internet Industrial de las Cosas (IIoT) ha impulsado el despliegue masivo de sensores, máquinas y sistemas ciberfísicos interconectados capaces de generar flujos continuos de datos heterogéneos. Aunque esta transformación permite la automatización inteligente, el mantenimiento predictivo y la optimización basada en datos, las arquitecturas tradicionales centralizadas en la nube no satisfacen los estrictos requisitos de baja latencia, escalabilidad y conciencia contextual exigidos por los entornos modernos de Industria 4.0. Afrontar estos desafíos requiere mecanismos de procesamiento distribuido, adaptativo y en tiempo real que operen a través de múltiples capas computacionales. Esta investigación doctoral propone una arquitectura colaborativa, consciente del contexto y de la situación, para el procesamiento heterogéneo de datos en tiempo real en ecosistemas IIoT. La principal contribución es el diseño e implementación de Atmosphere, un marco unificado edge–fog–cloud que integra sistemas multiagente, Procesamiento de Eventos Complejos (CEP) y Arquitectura Orientada a Servicios basada en Eventos (ED-SOA). En la capa edge, agentes software autónomos realizan filtrado local, razonamiento basado en reglas y toma de decisiones inmediata, reduciendo la sobrecarga de comunicación y la latencia. La capa fog incorpora mensajería basada en MQTT y motores CEP para agregar, correlacionar y analizar flujos de eventos distribuidos en tiempo casi real, permitiendo respuestas coordinadas entre nodos heterogéneos. La capa cloud proporciona analítica a gran escala, extracción de conocimiento a largo plazo, orquestación global y optimización estratégica. La arquitectura garantiza comunicación bidireccional, interoperabilidad y adaptación dinámica basada en información contextual. La segunda contribución valida la arquitectura propuesta mediante el diseño y desarrollo de un sistema robótico camuflado, alimentado por energía solar y basado en IoT, inspirado en el zorro del desierto Fennec. La plataforma robótica actúa como un nodo inteligente en la capa edge, capaz de adquisición autónoma de datos, toma de decisiones consciente del contexto y operación energéticamente eficiente en entornos hostiles y aislados. Las evaluaciones experimentales demuestran mejoras en capacidad de respuesta, escalabilidad y eficiencia operativa en comparación con enfoques tradicionales de monitorización centralizada. En conjunto, esta investigación avanza el estado del arte en analítica en tiempo real para IIoT al ofrecer un marco escalable, distribuido y consciente del contexto que integra fuentes de datos heterogéneas, mecanismos de coordinación inteligente y validación práctica. Las soluciones propuestas contribuyen al desarrollo de ecosistemas industriales resilientes, adaptativos e inteligentes alineados con los principios de la Industria 4.0."],"dc:format":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/10498/39800"],"dc:language.iso":["eng"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 Internacional"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:title":["Análisis en Tiempo Real para el Internet Industrial de las Cosas"],"dc:type":["doctoral thesis"]},"updated_at":"2026-07-24T01:29:38Z"}