Internet de las Cosas en Entornos Académicos. Caso de Éxito en la Universidad de Misiones

Lighting, ventilation, and air conditioning environments at the Universidad Nacional de Misiones are variables manually controlled even today. It is a fact that devices remain connected even in the absence of students/teachers, with no control or capacity of implement some action on temperatures or artificial/natural lighting. It is necessary to start an intelligent automated control of equipment and appliances, to reduce substantially the energy cost and to contribute to cut down the greenhouse effect. The proposed solution is carried out by using non-expensive components and the existing physical infrastructure in the location. The system performs monitoring, control of lighting and air condition equipment, as well as the presence of people in the environments. The control unit allows driving and monitoring the built modules by an application developed for the purpose.Actualmente, los equipos y artefactos destinados a la iluminación, ventilación y acondicionamiento del aire, en los ambientes de la Universidad de Misiones, se controlan de forma completamente manual. Se ha comprobado que dichos equipos y artefactos permanecen conectados inclusive en ausencia de alumnos y docentes, no existiendo a la fecha control y actuación alguna sobre la temperatura ambiente y/o iluminación. El presente trabajo introduce el desarrollo inicial de un sistema de control automatizado inteligente de equipos y aparatos, morigerando así costos y aportes al efecto invernadero. La solución planteada se lleva a cabo con ayuda de componentes económicos y usufructuando la infraestructura física existente en la dependencia. El sistema realiza el monitoreo, control de luminarias y de los equipos de acondicionamiento de aire, así como también, la presencia de personas en los ambientes. La central permite el accionamiento y control de los módulos en forma remota por medio de una aplicación desarrollada al efecto

Activity-Based Recommendations for Demand Response in Smart Sustainable Buildings

The energy consumption of private households amounts to approximately 30% of the total global energy consumption, causing a large share of the CO2 emissions through energy production. An intelligent demand response via load shifting increases the energy efficiency of residential buildings by nudging residents to change their energy consumption behavior. This paper introduces an activity prediction-based framework for the utility-based context-aware multi-agent recommendation system that generates an activity shifting schedule for a 24-hour time horizon to either focus on CO2 emissions or energy cost savings. In particular, we design and implement an Activity Agent that uses hourly energy consumption data. It does not require further sensorial data or activity labels which reduces implementation costs and the need for extensive user input. Moreover, the system enhances the utility option of saving energy costs by saving CO2 emissions and provides the possibility to focus on both dimensions. The empirical results show that while setting the focus on CO2 emissions savings, the system provides an average of 12% of emissions savings and 7% of cost savings. When focusing on energy cost savings, 20% of energy costs and 6% of emissions savings are possible for the studied households in case of accepting all recommendations. Recommending an activity schedule, the system uses the same terms residents describe their domestic life. Therefore, recommendations can be more easily integrated into daily life supporting the acceptance of the system in a long-term perspective

Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review

The Human Activity Recognition (HAR) tasks automatically identify human activities using the sensor data, which has numerous applications in healthcare, sports, security, and human-computer interaction. Despite significant advances in HAR, critical challenges still exist. Game theory has emerged as a promising solution to address these challenges in machine learning problems including HAR. However, there is a lack of research work on applying game theory solutions to the HAR problems. This review paper explores the potential of game theory as a solution for HAR tasks, and bridges the gap between game theory and HAR research work by suggesting novel game-theoretic approaches for HAR problems. The contributions of this work include exploring how game theory can improve the accuracy and robustness of HAR models, investigating how game-theoretic concepts can optimize recognition algorithms, and discussing the game-theoretic approaches against the existing HAR methods. The objective is to provide insights into the potential of game theory as a solution for sensor-based HAR, and contribute to develop a more accurate and efficient recognition system in the future research directions

Quality of Experience monitoring and management strategies for future smart networks

One of the major driving forces of the service and network's provider market is the user's perceived service quality and expectations, which are referred to as user's Quality of Experience (QoE). It is evident that QoE is particularly critical for network providers, who are challenged with the multimedia engineering problems (e.g. processing, compression) typical of traditional networks. They need to have the right QoE monitoring and management mechanisms to have a significant impact on their budget (e.g. by reducing the users‘ churn). Moreover, due to the rapid growth of mobile networks and multimedia services, it is crucial for Internet Service Providers (ISPs) to accurately monitor and manage the QoE for the delivered services and at the same time keep the computational resources and the power consumption at low levels. The objective of this thesis is to investigate the issue of QoE monitoring and management for future networks. This research, developed during the PhD programme, aims to describe the State-of-the-Art and the concept of Virtual Probes (vProbes). Then, I proposed a QoE monitoring and management solution, two Agent-based solutions for QoE monitoring in LTE-Advanced networks, a QoE monitoring solution for multimedia services in 5G networks and an SDN-based approach for QoE management of multimedia services

User activity recognition for energy saving in smart home environment

In recent years, the consumption of electricity has increased considerably in the industrial, commercial and residential sectors. This has prompted a branch of research which attempts to overcome this problem by applying different information and communication technologies, turning houses and buildings into smart environments. In this paper, we propose and design an energy saving technique based on the relationship between the user's activities and electrical appliances in smart home environments. The proposed method utilizes machine learning techniques to automatically recognize the user's activities, and then a ranking algorithm is applied to relate activities and existing home appliances. Finally, the system gives recommendations to the user whenever it detects a waste of energy. Tests on a real database show that the proposed method can to save up to 35% of electricity in a smart home