{"id":{"repo_id":"cadiz","oai_identifier":"oai:rodin.uca.es:10498/39860"},"canonical_url":"https://search.dev.ndltd.org/etd/cadiz/oai:rodin.uca.es:10498/39860","repository":{"repo_id":"cadiz","name":"Universidad de Cadiz","base_url":"https://rodin.uca.es/oai/request"},"display":{"title":"Arquitectura Software para el Enriquecimiento del Contexto y Detección de Eventos Significativos en Dominios Smart Everything del Internet de las Cosas Colaborativo","abstract":"The exponential growth of the Internet of Things (IoT) and the proliferation of Smart Everything applications have driven the development of intelligent environments capable of monitoring, processing and reacting to large volumes of data in real time. However, most current solutions continue to operate in isolation within a single application domain, which limits information contextualisation and misses a significant opportunity to improve it by discarding data from domains without an apparent relationship. Furthermore, the absence of a common criterion for defining and structuring data generated across different domains hinders their integration and correlation. This doctoral thesis addresses these limitations by proposing a software architecture aimed at improving contextualisation through the detection of situations of interest and their management via contextualised actions in collaborative IoT and Smart Everything environments. First, a taxonomy is defined that enables the homogeneous definition of data from multiple IoT domains, providing a set of extensible common attributes that can adapt to specific requirements without losing semantic coherence. Based on this taxonomy, a JSON-based data structure is designed to facilitate the structured exchange of information among different IoT domains. Building on this taxonomy and data structure, a software architecture is proposed that integrates messaging brokers, real-time processing through Complex Event Processing (CEP), and storage and notification systems. This architecture enables the integration and correlation of simple events from different domains to detect situations of interest in real time, thereby improving contextualisation in multi-domain environments. However, it is not possible to define contextualised actions exclusively at the CEP engine level. Moreover, CEP engines can become complex and difficult to maintain as the number of CEP patterns and situations of interest to be detected increases. To overcome this limitation, the thesis incorporates Business Process Management (BPM) as a complementary mechanism within the proposed software architecture for the definition and management of contextualised actions, as well as for the definition of the CEP pattern logic and their real-time updates. This integration improves the maintainability, adaptability and usability of the system, facilitating its evolution in response to changes in context or domain requirements. The feasibility and usefulness of the proposal are validated through the implementation of the software architecture and the development of representative case studies, together with performance and usability evaluations. The results obtained demonstrate that the solution enables the integration of data from heterogeneous domains, improves information contextualisation, and manages intelligent actions in a more scalable and maintainable way when handling an increasing number of situations of interest compared with traditional solutions based on isolated domains. In summary, this thesis presents a proposal that combines a multi-domain taxonomy, a CEP-based software architecture, and BPM integration for the management of contextualised actions in collaborative IoT and Smart Everything environments.","abstract_html":"The exponential growth of the Internet of Things (IoT) and the proliferation of Smart Everything applications have driven the development of intelligent environments capable of monitoring, processing and reacting to large volumes of data in real time. However, most current solutions continue to operate in isolation within a single application domain, which limits information contextualisation and misses a significant opportunity to improve it by discarding data from domains without an apparent relationship. Furthermore, the absence of a common criterion for defining and structuring data generated across different domains hinders their integration and correlation. This doctoral thesis addresses these limitations by proposing a software architecture aimed at improving contextualisation through the detection of situations of interest and their management via contextualised actions in collaborative IoT and Smart Everything environments. First, a taxonomy is defined that enables the homogeneous definition of data from multiple IoT domains, providing a set of extensible common attributes that can adapt to specific requirements without losing semantic coherence. Based on this taxonomy, a JSON-based data structure is designed to facilitate the structured exchange of information among different IoT domains. Building on this taxonomy and data structure, a software architecture is proposed that integrates messaging brokers, real-time processing through Complex Event Processing (CEP), and storage and notification systems. This architecture enables the integration and correlation of simple events from different domains to detect situations of interest in real time, thereby improving contextualisation in multi-domain environments. However, it is not possible to define contextualised actions exclusively at the CEP engine level. Moreover, CEP engines can become complex and difficult to maintain as the number of CEP patterns and situations of interest to be detected increases. To overcome this limitation, the thesis incorporates Business Process Management (BPM) as a complementary mechanism within the proposed software architecture for the definition and management of contextualised actions, as well as for the definition of the CEP pattern logic and their real-time updates. This integration improves the maintainability, adaptability and usability of the system, facilitating its evolution in response to changes in context or domain requirements. The feasibility and usefulness of the proposal are validated through the implementation of the software architecture and the development of representative case studies, together with performance and usability evaluations. The results obtained demonstrate that the solution enables the integration of data from heterogeneous domains, improves information contextualisation, and manages intelligent actions in a more scalable and maintainable way when handling an increasing number of situations of interest compared with traditional solutions based on isolated domains. In summary, this thesis presents a proposal that combines a multi-domain taxonomy, a CEP-based software architecture, and BPM integration for the management of contextualised actions in collaborative IoT and Smart Everything environments.","abstract_has_math":false,"creators":["Bazán Muñoz, Adrián"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Ortiz Bellot, Guadalupe","García de Prado Fontela, Alfonso"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T01:29:41Z","subjects":["Context Awareness","Consciencia del contexto","Internet of Things","Internet de las Cosas","Business Process Model","Modelo de Procesos de Negocio","Complex Event Processing","Procesamiento de Eventos Complejos","Collaborative Internet of Things","Internet de las Cosas Colaborativo","Taxonomy","Taxonomía"],"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/10498/39860","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","García de Prado Fontela, Alfonso"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Ingeniería en Automática, Electrónica, Arquitectura y Redes de Computadores","Ingeniería Informática"]},{"key":"dc:creator","label":"Author","values":["Bazán Muñoz, Adrián"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-24T10:19:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-06-24T10:19:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["doctoral thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Context Awareness","Consciencia del contexto","Internet of Things","Internet de las Cosas","Business Process Model","Modelo de Procesos de Negocio","Complex Event Processing","Procesamiento de Eventos Complejos","Collaborative Internet of Things","Internet de las Cosas Colaborativo","Taxonomy","Taxonomía"]}]},{"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/10498/39860"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The exponential growth of the Internet of Things (IoT) and the proliferation of Smart Everything applications have driven the development of intelligent environments capable of monitoring, processing and reacting to large volumes of data in real time. However, most current solutions continue to operate in isolation within a single application domain, which limits information contextualisation and misses a significant opportunity to improve it by discarding data from domains without an apparent relationship. Furthermore, the absence of a common criterion for defining and structuring data generated across different domains hinders their integration and correlation. This doctoral thesis addresses these limitations by proposing a software architecture aimed at improving contextualisation through the detection of situations of interest and their management via contextualised actions in collaborative IoT and Smart Everything environments. First, a taxonomy is defined that enables the homogeneous definition of data from multiple IoT domains, providing a set of extensible common attributes that can adapt to specific requirements without losing semantic coherence. Based on this taxonomy, a JSON-based data structure is designed to facilitate the structured exchange of information among different IoT domains. Building on this taxonomy and data structure, a software architecture is proposed that integrates messaging brokers, real-time processing through Complex Event Processing (CEP), and storage and notification systems. This architecture enables the integration and correlation of simple events from different domains to detect situations of interest in real time, thereby improving contextualisation in multi-domain environments. However, it is not possible to define contextualised actions exclusively at the CEP engine level. Moreover, CEP engines can become complex and difficult to maintain as the number of CEP patterns and situations of interest to be detected increases. To overcome this limitation, the thesis incorporates Business Process Management (BPM) as a complementary mechanism within the proposed software architecture for the definition and management of contextualised actions, as well as for the definition of the CEP pattern logic and their real-time updates. This integration improves the maintainability, adaptability and usability of the system, facilitating its evolution in response to changes in context or domain requirements. The feasibility and usefulness of the proposal are validated through the implementation of the software architecture and the development of representative case studies, together with performance and usability evaluations. The results obtained demonstrate that the solution enables the integration of data from heterogeneous domains, improves information contextualisation, and manages intelligent actions in a more scalable and maintainable way when handling an increasing number of situations of interest compared with traditional solutions based on isolated domains. In summary, this thesis presents a proposal that combines a multi-domain taxonomy, a CEP-based software architecture, and BPM integration for the management of contextualised actions in collaborative IoT and Smart Everything environments.","El crecimiento exponencial del Internet de las Cosas (IoT) y la proliferación de aplicaciones Smart Everything han impulsado el desarrollo de entornos inteligentes capaces de monitorizar, procesar y reaccionar ante grandes volúmenes de datos en tiempo real. Sin embargo, la mayoría de las soluciones actuales continúan operando de forma aislada dentro de un único dominio de aplicación, lo que limita la contextualización de la información, perdiendo una gran oportunidad de mejorar la contextualización al descartar datos de dominios sin una aparente relación. Además, la ausencia de un criterio común para definir y estructurar los datos generados en distintos dominios dificulta su integración y correlación. Esta tesis doctoral aborda estas limitaciones proponiendo una arquitectura software orientada a mejorar la contextualización mediante la detección de situaciones de interés y su gestión mediante acciones contextualizadas en entornos Smart Everything del IoT colaborativo. En primer lugar, se define una taxonomía que permite definir de forma homogénea los datos procedentes de múltiples dominios IoT, proporcionando un conjunto de atributos comunes extensibles que permiten adaptarse a requisitos específicos sin perder coherencia semántica. A partir de esta taxonomía, se diseña una estructura de datos basada en JSON que facilita el intercambio estructurado de información entre diferentes dominios del IoT. Sobre esta taxonomía y estructura de datos, se propone una arquitectura software que integra brókeres de mensajería, procesamiento en tiempo real mediante Procesamiento de Eventos Complejos (CEP) y sistemas de almacenamiento y notificación. Esta arquitectura permite la integración y correlación de eventos simples procedentes de distintos dominios para la detección de situaciones de interés en tiempo real, permitiendo mejorar la contextualización en entornos multidominio. No obstante, no es posible definir las acciones contextualizadas exclusivamente a nivel del motor CEP. Además los motores CEP pueden ser sistemas complejos y difíciles de mantener cuando aumenta el número de patrones CEP y situaciones de interés a detectar. Para superar esta limitación, la tesis incorpora Business Process Management (BPM) como mecanismo complementario en la arquitectura software propuesta para la definición y gestión de acciones contextualizadas, así como para la definición de la lógica de los patrones CEP y de sus actualizaciones en tiempo real. Esta integración mejora la mantenibilidad, adaptabilidad y usabilidad del sistema, facilitando la adaptación del sistema ante cambios en el contexto o en los requisitos del dominio. La viabilidad y utilidad de la propuesta se validan mediante la implementación de la arquitectura software y el desarrollo de casos de estudio representativos, junto con evaluaciones de rendimiento y usabilidad. Los resultados obtenidos demuestran que la solución permite integrar datos de dominios heterogéneos, mejorar la contextualización de la información y gestionar acciones inteligentes de forma más escalable y mantenible que las soluciones tradicionales basadas en dominios aislados. En síntesis, esta tesis presenta una propuesta que combina una taxonomía multidominio, una arquitectura software basada en CEP y la integración de BPM para la gestión de acciones contextualizadas en entornos IoT colaborativos y Smart Everything"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Arquitectura Software para el Enriquecimiento del Contexto y Detección de Eventos Significativos en Dominios Smart Everything del Internet de las Cosas Colaborativo"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ortiz Bellot, Guadalupe","García de Prado Fontela, Alfonso"],"dc:contributor.other":["Ingeniería en Automática, Electrónica, Arquitectura y Redes de Computadores","Ingeniería Informática"],"dc:creator":["Bazán Muñoz, Adrián"],"dc:date.accessioned":["2026-06-24T10:19:37Z"],"dc:date.available":["2026-06-24T10:19:37Z"],"dc:date.issued":["2026"],"dc:description.abstract":["The exponential growth of the Internet of Things (IoT) and the proliferation of Smart Everything applications have driven the development of intelligent environments capable of monitoring, processing and reacting to large volumes of data in real time. However, most current solutions continue to operate in isolation within a single application domain, which limits information contextualisation and misses a significant opportunity to improve it by discarding data from domains without an apparent relationship. Furthermore, the absence of a common criterion for defining and structuring data generated across different domains hinders their integration and correlation. This doctoral thesis addresses these limitations by proposing a software architecture aimed at improving contextualisation through the detection of situations of interest and their management via contextualised actions in collaborative IoT and Smart Everything environments. First, a taxonomy is defined that enables the homogeneous definition of data from multiple IoT domains, providing a set of extensible common attributes that can adapt to specific requirements without losing semantic coherence. Based on this taxonomy, a JSON-based data structure is designed to facilitate the structured exchange of information among different IoT domains. Building on this taxonomy and data structure, a software architecture is proposed that integrates messaging brokers, real-time processing through Complex Event Processing (CEP), and storage and notification systems. This architecture enables the integration and correlation of simple events from different domains to detect situations of interest in real time, thereby improving contextualisation in multi-domain environments. However, it is not possible to define contextualised actions exclusively at the CEP engine level. Moreover, CEP engines can become complex and difficult to maintain as the number of CEP patterns and situations of interest to be detected increases. To overcome this limitation, the thesis incorporates Business Process Management (BPM) as a complementary mechanism within the proposed software architecture for the definition and management of contextualised actions, as well as for the definition of the CEP pattern logic and their real-time updates. This integration improves the maintainability, adaptability and usability of the system, facilitating its evolution in response to changes in context or domain requirements. The feasibility and usefulness of the proposal are validated through the implementation of the software architecture and the development of representative case studies, together with performance and usability evaluations. The results obtained demonstrate that the solution enables the integration of data from heterogeneous domains, improves information contextualisation, and manages intelligent actions in a more scalable and maintainable way when handling an increasing number of situations of interest compared with traditional solutions based on isolated domains. In summary, this thesis presents a proposal that combines a multi-domain taxonomy, a CEP-based software architecture, and BPM integration for the management of contextualised actions in collaborative IoT and Smart Everything environments.","El crecimiento exponencial del Internet de las Cosas (IoT) y la proliferación de aplicaciones Smart Everything han impulsado el desarrollo de entornos inteligentes capaces de monitorizar, procesar y reaccionar ante grandes volúmenes de datos en tiempo real. Sin embargo, la mayoría de las soluciones actuales continúan operando de forma aislada dentro de un único dominio de aplicación, lo que limita la contextualización de la información, perdiendo una gran oportunidad de mejorar la contextualización al descartar datos de dominios sin una aparente relación. Además, la ausencia de un criterio común para definir y estructurar los datos generados en distintos dominios dificulta su integración y correlación. Esta tesis doctoral aborda estas limitaciones proponiendo una arquitectura software orientada a mejorar la contextualización mediante la detección de situaciones de interés y su gestión mediante acciones contextualizadas en entornos Smart Everything del IoT colaborativo. En primer lugar, se define una taxonomía que permite definir de forma homogénea los datos procedentes de múltiples dominios IoT, proporcionando un conjunto de atributos comunes extensibles que permiten adaptarse a requisitos específicos sin perder coherencia semántica. A partir de esta taxonomía, se diseña una estructura de datos basada en JSON que facilita el intercambio estructurado de información entre diferentes dominios del IoT. Sobre esta taxonomía y estructura de datos, se propone una arquitectura software que integra brókeres de mensajería, procesamiento en tiempo real mediante Procesamiento de Eventos Complejos (CEP) y sistemas de almacenamiento y notificación. Esta arquitectura permite la integración y correlación de eventos simples procedentes de distintos dominios para la detección de situaciones de interés en tiempo real, permitiendo mejorar la contextualización en entornos multidominio. No obstante, no es posible definir las acciones contextualizadas exclusivamente a nivel del motor CEP. Además los motores CEP pueden ser sistemas complejos y difíciles de mantener cuando aumenta el número de patrones CEP y situaciones de interés a detectar. Para superar esta limitación, la tesis incorpora Business Process Management (BPM) como mecanismo complementario en la arquitectura software propuesta para la definición y gestión de acciones contextualizadas, así como para la definición de la lógica de los patrones CEP y de sus actualizaciones en tiempo real. Esta integración mejora la mantenibilidad, adaptabilidad y usabilidad del sistema, facilitando la adaptación del sistema ante cambios en el contexto o en los requisitos del dominio. La viabilidad y utilidad de la propuesta se validan mediante la implementación de la arquitectura software y el desarrollo de casos de estudio representativos, junto con evaluaciones de rendimiento y usabilidad. Los resultados obtenidos demuestran que la solución permite integrar datos de dominios heterogéneos, mejorar la contextualización de la información y gestionar acciones inteligentes de forma más escalable y mantenible que las soluciones tradicionales basadas en dominios aislados. En síntesis, esta tesis presenta una propuesta que combina una taxonomía multidominio, una arquitectura software basada en CEP y la integración de BPM para la gestión de acciones contextualizadas en entornos IoT colaborativos y Smart Everything"],"dc:format":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/10498/39860"],"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:subject":["Context Awareness","Consciencia del contexto","Internet of Things","Internet de las Cosas","Business Process Model","Modelo de Procesos de Negocio","Complex Event Processing","Procesamiento de Eventos Complejos","Collaborative Internet of Things","Internet de las Cosas Colaborativo","Taxonomy","Taxonomía"],"dc:title":["Arquitectura Software para el Enriquecimiento del Contexto y Detección de Eventos Significativos en Dominios Smart Everything del Internet de las Cosas Colaborativo"],"dc:type":["doctoral thesis"]},"updated_at":"2026-07-24T01:29:41Z"}