Energy Efficiency in Public Buildings through Context-Aware Social Computing

[EN]The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings.European Commision (EC). Funding H2020/MSCARISE. Project Code: 64179

A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring

[EN]Real-time Localization Systems have been postulated as one of the most appropriated technologies for the development of applications that provide customized services. These systems provide us with the ability to locate and trace users and, among other features, they help identify behavioural patterns and habits. Moreover, the implementation of policies that will foster energy saving in homes is a complex task that involves the use of this type of systems. Although there are multiple proposals in this area, the implementation of frameworks that combine technologies and use Social Computing to influence user behaviour have not yet reached any significant savings in terms of energy. In this work, the CAFCLA framework (Context-Aware Framework for Collaborative Learning Applications) is used to develop a recommendation system for home users. The proposed system integrates a Real-Time Localization System and Wireless Sensor Networks, making it possible to develop applications that work under the umbrella of Social Computing. The implementation of an experimental use case aided efficient energy use, achieving savings of 17%. Moreover, the conducted case study pointed to the possibility of attaining good energy consumption habits in the long term. This can be done thanks to the system’s real time and historical localization, tracking and contextual data, based on which customized recommendations are generated.European Commision (EC). Funding H2020/MSCARISE. Project Code: 64179

A New Stability Criterion for IoT Systems in Smart Buildings: Temperature Case Study

The concept of smart cities emerged in the 1990s. Since then, smart buildings have become a closely interconnected element of smart cities. This type of building implements Internet of Things technology and control algorithms to monitor and control their indoor environment. The aim of this paper is to develop a new stability criterion method for smart building Internet of Things (IoT) systems, subject to external disturbances. The new stability criterion is going to optimize the operation of control algorithms since this criterion does not depend on the transmission function of the control algorithm but on the data collected by the IoT system. We present a new matrix called “Laplacian IoT matrix”, containing IoT network information associated with the graph of a smart building. The proposal is supported by the results of a numerical case study.This work was developed as part of “Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT sobre organizaciones virtuales de agentes ligeros y su aplicación en la eficiencia en el transporte de última milla”, ID SA267P18, project cofinanced by Junta Castilla y León, Consejería de Educación, and FEDER funds

Innovación y clústeres tecnológicos

[ES] El taller sobre tecnologías de información y comunicación disruptivas para la innovación y la transformación digital, organizado bajo el alcance del proyecto disruptiva, tiene como objetivo discutir los problemas, desafíos y beneficios del uso de tecnologías digitales disruptivas, a saber, Internet de las cosas, Big data, computación en la nube, sistemas multi-agentes, aprendizaje automático, realidad virtual y aumentada, y robótica colaborativa, para apoyar la transformación digital en curso en la sociedad Temas Intelligent Manufacturing Systems Industry 4.0 and digital transformation Internet of Things Cyber-security Collaborative and intelligent robotics Multi-Agent Systems Industrial Cyber-Physical Systems Virtualization and digital twins Predictive maintenance Virtual and augmented reality Big Data and advanced data analytics Edge and cloud computing[EN] The workshop on Disruptive Information and Communication Technologies for Innovation and Digital transformation, organized under the scope of the Disruptive project, aims to discuss problems, challenges and benefits of using disruptive digital technologies, namely Internet of Things, Big data, cloud computing, multi-agent systems, machine learning, virtual and augmented reality, and collaborative robotics, to support the on-going digital transformation in society Topics Intelligent Manufacturing Systems Industry 4.0 and digital transformation Internet of Things Cyber-security Collaborative and intelligent robotics Multi-Agent Systems Industrial Cyber-Physical Systems Virtualization and digital twins Predictive maintenance Virtual and augmented reality Big Data and advanced data analytics Edge and cloud computin

Improving an Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care using Wireless Sensor Networks

This paper describes last improvements made on ALZ-MAS; an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients. The system makes use of several context-aware technologies that allow it to automatically obtain information from users and the environment in an evenly distributed way, focusing on the characteristics of ubiquity, awareness, intelligence, mobility, etc., all of which are concepts defined by Ambient Intelligence. Among these context-aware technologies we have Wireless Sensor Networks. In this sense, ALZ-MAS is currently being improved by the use of a new platform of ZigBee devices that provides the system with new telemonitoring and locating engine

A Case-Based Planning Mechanism for a Hardware-Embedded Reactive Agents Platform

Wireless Sensor Networks is a key technology for gathering relevant in- formation from different sources. In this sense, Multi-Agent Systems can facilitate the integration of heterogeneous sensor networks and expand the sensors’ capa- bilities changing their behavior dynamically and personalizing their reactions. Both Wireless Sensor Networks and Multi-Agent Systems can be successfully applied to different management scenarios, such as logistics, supply chain or pro- duction. The Hardware-Embedded Reactive Agents (HERA) platform allows developing applications where agents are directly embedded in heterogeneous wireless sensor nodes with reduced computational resources. This paper presents the reasoning mechanism included in HERA to provide HERA Agents with Case- Based Planning features that allow solving problems considering past experiences.

ARTIZT: Applying Ambient Intelligence to a Museum Guide Scenario

Museum guides present a great opportunity where the Ambient Intelligence (AmI) paradigm can be successfully applied. Together with pervasive computing, context and location awareness are the AmI features that allow users to receive customized information in a transparent way. In this sense, Real-Time Locating Systems (RTLS) can improve context-awareness in AmI-based systems. This paper presents ARTIZT, an innovative AmI-based museum guide system where a novel RTLS based on the ZigBee protocol provides highly precise users’ position information. Thus, it can be customized the content offered to the users without their explicit interaction, as well as the granularity level provided by the system

Supporting Context-Aware Collaborative Learning Activities by CAFCLA

The integration of Information and Communication Technologies (ICT) in daily life has improved the learning process by means of context-aware technologies. Through the use of technology, new ways of learning has emerged allowing to become the learning process more ubiquitous. However, it is necessary to develop new tools that can be adapted to a wide range of technologies and different application scenarios. This paper presents CAFCLA, a framework that allows developing context-aware learning applications. CAFCLA integrates different context-aware technologies, so that learning applications designed, developed and deployed upon it are dynamic, adaptive and easy to use by users such as students and teachers

Improving Context-Awareness in a Healthcare Multi-Agent System

Context-aware technologies allow Ambient Assisted Living systems and applications to automatically obtain information from users and their environment in a distributed and ubiquitous way. One of the most important technologies used to provide context-awareness to a system is Wireless Sensor Networks. This paper describes last improvements made on ALZ-MAS, an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and healthcare for Alzheimer patients. In this sense, a new ZigBee platform is used to improve ALZ-MAS. This platform provides the system with new telemonitoring and locating engines that facilitate the integration of context-awareness into it

Implementing a real-time locating system based on wireless sensor networks and artificial neural networks to mitigate the multipath effect

Wireless Sensor Networks comprise an ideal technology to develop Real-Time Locating Systems (RTLSs) aimed at indoor environments, where existing global navigation satellite systems do not work correctly due to the blockage of the satellite signals. In this regard, one of the main challenges is to deal with the problems that arise from the effects of the propagation of radio frequency waves, such as multipath. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the multipath effect by using Multi-Layer Perceptron Artificial Neural Networks. The model is used to implement a novel indoor Real-Time Locating System based on Wireless Sensor Networks