A Resource-based Rule Engine for energy savings recommendations in Educational Buildings

Raising awareness among young people on the relevance of behaviour change for achieving energy savings is widely considered as a key approach towards long-term and cost-effective energy efficiency policies. The GAIA Project aims to deliver a comprehensive solution for both increasing awareness on energy efficiency and achieving energy savings in school buildings. In this framework, we present a novel rule engine that, leveraging a resource-based graph model encoding relevant application domain knowledge, accesses IoT data for producing energy savings recommendations. The engine supports configurability, extensibility and ease-of-use requirements, to be easily applied and customized to different buildings. The paper introduces the main design and implementation details and presents a set of preliminary performance results

Modelo adaptativo de inteligencia ambiental sensible al contexto basado en ontologías, agentes inteligentes y redes de sensores inalámbricos

Currently, healthcare is a crucial issue for the entire population, especially for individuals who suffer from a chronic disease such as hypertension or diabetes. However, this care is carried out in medical centers, limiting the scope of health professionals. In fact, some monitoring, early warning processes, and health supporting that are not presently performed, could be carried out at the patient's location. The aim of this master's thesis is to integrate wireless sensors networks (WSN), ambient intelligence, multi-agent systems, and ontologies, in order to develop an ambient intelligence model that is context sensitive and that provides alerts, personalized recommendations, and adaptive health-care agendas. Personalized agendas based on chronic patient profiles offer appropriate physical activity, personalized food diet, and specific activities in order to control stress levels. For the validation of the proposed model, a prototype was constructed and applied to two case study considering several chronic patients. The results demonstrate the effectiveness of the proposed health-care ambient intelligence multi-agent modelActualmente, la atención médica es un tema gran importancia para toda la población, especialmente para las personas que padecen una enfermedad crónica como la hipertensión o la diabetes. Sin embargo, esta atención se lleva a cabo en centros médicos, lo que limita el alcance de los profesionales de la salud. De hecho, algunos controles, procesos de alerta temprana y apoyo de salud que no se realizan actualmente, podrían llevarse a cabo en la ubicación del paciente. El objetivo de esta tesis de maestría es integrar redes de sensores inalámbricos (WSN), inteligencia ambiental, sistemas de múltiples agentes y ontologías, con el fin de desarrollar un modelo de inteligencia ambiental que sea sensible al contexto y proporcione alertas, recomendaciones personalizadas y agendas adaptativas de atención médica. Las agendas personalizadas basadas en perfiles pacientes con enfermedades crónicas ofrecen actividad física adecuada, dieta alimentaria personalizada y actividades específicas para controlar los niveles de estrés. Para la validación del modelo propuesto, se construyó un prototipo y se aplicó a dos casos de estudios considerando varios pacientes crónicos. Los resultados demuestran la efectividad del modelo multi-agente de inteligencia ambiental propuesto para la atención médica.Magister en Ingeniería - Ingeniería de SistemasMaestrí