Detección de rutinas : internet of people. Prueba de concepto

El principal objetivo de Internet of Things (IoT) es integrar las tecnologías informáticas en el quehacer cotidiano de las personas, facilitando su interacción con un entorno de dispositivos interconectados, pero el estado actual del arte hace que dicha interacción esté aún lejos de resultar trivial, precisando de continua intervención del usuario. Como alternativa a esta situación, iniciativas emergentes como la de Internet of People (IoP) pretenden integrar de forma más efectiva el IoT en la vida de las personas. En línea con este propósito, el modelo People as a Service (PeaaS) facilita estas tareas por medio del uso del teléfono móvil como interfaz del usuario con el IoT y haciendo uso del contexto del usuario del mismo. PeaaS permite elaborar un perfil sociológico del usuario, que puede ser explotado por el mismo y servido a terceros de forma segura y controlada. En este trabajo presentamos una aplicación móvil para la supervisión de personas afectadas de alzhéimer mediante el aprendizaje y monitorización de sus rutinas como prueba de concepto del modelo PeaaS, teniendo como resultado una funcionalidad que va mucho más allá de la ofrecida por otros productos similares en este campo, y una tecnología que es base para infinidad de aplicaciones que provoquen el avance hacia IoP.

Elastic Computing - a novel paradigm for distributed systems

This talk, which is based on our newest findings and experiences from research and industrial projects, addresses one of the most relevant challenges for a decade to come: How to integrate the Internet of Things with software, people, and processes, considering modern Cloud Computing and Elasticity principles. Elasticity is seen as one of the main characteristics of Cloud Computing today. Is elasticity simply scalability on steroids? This talk addresses the main principles of elasticity, presents a fresh look at this problem, and examines how to integrate people, software services, and things into one composite system, which can be modeled, programmed, and deployed on a large scale in an elastic way. This novel paradigm has major consequences on how we view, build, design, and deploy ultra-large scale distributed systems.

Using SPL to Develop AAL Systems Based on Self-adaptive Agents

Abstract One of the most important challenges of this decade is the Internet of Things (IoT) that pursues the integration of real-world objects in Internet. One of the key areas of the IoT is the Ambient Assisted Living (AAL) systems, which should be able to react to variable and continuous changes while ensuring their acceptance and adoption by users. This means that AAL systems need to work as self-adaptive systems. The autonomy property inherent to software agents, makes them a suitable choice for developing self-adaptive systems. However, agents lack the mechanisms to deal with the variability present in the IoT domain with regard to devices and network technologies. To overcome this limitation we have already proposed a Software Product Line (SPL) process for the development of self-adaptive agents in the IoT. Here we analyze the challenges that poses the development of self-adaptive AAL systems based on agents. To do so, we focus on the domain and application engineering of the self-adaptation concern of our SPL process. In addition, we provide a validation of our development process for AAL systems.

Situational-Context: A Unified View of Everything Involved at a Particular Situation

As the interest in the Web of Things increases, specially for the general population, the barriers to entry for the use of these technologies should decrease. Current applications can be developed to adapt their behaviour to predefined conditions and users preferences, facilitating their use. In the future,Web of Things software should be able to automatically adjust its behaviour to non-predefined preferences or context of its users. In this vision paper we define the Situational-Context as the combination of the virtual profiles of the entities (things or people) that concur at a particular place and time. The computation of the Situational-Context allow us to predict the expected system behaviour and the required interaction between devices to meet the entities’ goals, achieving a better adjustment of the system to variable contexts.

Towards a unified management of applications on heterogeneous clouds

The diversity in the way cloud providers o↵er their services, give their SLAs, present their QoS, or support di↵erent technologies, makes very difficult the portability and interoperability of cloud applications, and favours the well-known vendor lock-in problem. We propose a model to describe cloud applications and the required resources in an agnostic, and providers- and resources-independent way, in which individual application modules, and entire applications, may be re-deployed using different services without modification. To support this model, and after the proposal of a variety of cross-cloud application management tools by different authors, we propose going one step further in the unification of cloud services with a management approach in which IaaS and PaaS services are integrated into a unified interface. We provide support for deploying applications whose components are distributed on different cloud providers, indistinctly using IaaS and PaaS services.

Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living

The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results.

An assisted navigation method for telepresence robots

Telepresence robots have emerged as a new means of interaction in remote environments. However, the use of such robots is still limited due to safety and usability issues when operating in human-like environments. This work addresses these issues by enhancing the robot navigation through a collaborative control method that assists the user to negotiate obstacles. The method has been implemented in a commercial telepresence robot and a user study has been conducted in order to test the suitability of our approach.

Enhancing smart environments with mobile robots

Sensor networks are becoming popular nowadays in the development of smart environments. Heavily relying on static sensor and actuators, though, such environments usually lacks of versatility regarding the provided services and interaction capabilities. Here we present a framework for smart environments where a service robot is included within the sensor network acting as a mobile sensor and/or actuator. Our framework integrates on-the-shelf technologies to ensure its adaptability to a variety of sensor technologies and robotic software. Two pilot cases are presented as evaluation of our proposal.