Event recording system for smart space applications

Smart space applications consist out of several agents interacting with each other. Their operation form an ubiquitous environment assisting actions of the user. There could possibly be several applications running at the same time supporting different human activities. In this paper we address the issues of identification of meaningful event, context information gathering and visualization with the use of a developed tool. The application allows to display the current or recorded context information using the time line visualization approach

Conception de tableaux de bord adaptatifs pour les habitats intelligents

Le vieillissement de la population apporte des transformations sociales qui affectent de plus en plus nos sociétés. Les évolutions technologiques soutiennent cette évolution sociale profonde et ce phénomène est destiné à croître dans les prochaines années. Un des effets de cette transformation est le nombre croissant de personnes en perte d’autonomie qui nécessite un soutien et une assistance au quotidien. Les habitats intelligents se présentent de plus en plus comme une solution intéressante pouvant répondre à une partie du problème. En effet, ils disposent d’une panoplie de capteurs et de fonctionnalités qui, combinés à l’intelligence artificielle, ont le potentiel d'assister leurs occupants au quotidien. Cependant, la complexité des systèmes et la nature brute des données ne sont pas abordables pour tous les publics. Au centre de cette solution, plusieurs sujets sont abordés : la visualisation de données, la conception d’applications et l'interfaçage humain-machine. Cette composition vise à proposer une conception d’applications modulables et flexibles afin d’assister de manière appropriée les utilisateurs du système. Parmi les solutions exploitables, les tableaux de bord ont généré beaucoup d'intérêt car ils offrent un équilibre entre adaptabilité et facilité d’utilisation. Les travaux couverts par ce mémoire visent à proposer des outils de visualisation adaptatifs pour les habitats intelligents en mettant l’accent sur la modularité des composants. Pour ce faire, plusieurs étapes ont été implémentées. Dans un premier temps, une méthode de modularisation des applications a permis de diviser le problème en plusieurs modules et d’échanger leurs résultats de manière bidirectionnelle. Dans un second temps, nous avons développé une application qui intègre ces modules. Finalement, nous avons conçu une application de visualisation capable de fournir des données adaptées aux besoins. Enfin, nous avons testé l’application auprès de plusieurs participants dans le but d’évaluer l'efficacité de notre méthode. Nous avons obtenu un score de 85.3 en termes d’utilisabilité selon notre questionnaire SUS. The aging of the population brings social transformations that increasingly affect our societies. Technological developments support this profound social evolution and this phenomenon will undoubtedly grow in the coming years. One of the effects of this transformation is the growing number of people with a loss of autonomy who require support and assistance on a daily basis. Smart homes are increasingly presenting themselves as an interesting solution that can solve part of the problem. Indeed, they have a range of sensors and functionalities which, combined with artificial intelligence, have the potential to assist their occupants on a daily basis. However, the complexity of the systems and the raw nature of the data are not affordable for all audiences. At the center of this solution, several topics are covered: data visualization, application design and human machine interfacing. Th is composition aims to propose a design of modular and flexible applications in order to appropriately assist the users of the system. Among the actionable solutions, dashboards have generated a lot of interest because they offer a balance between adaptability and ease of use. The work covered by this master aims to provide adaptive visualization tools for smart homes with an emphasis on the modularity of components. To do this, several steps have been implemented. At first, a method of modularization of t he applications made it possible to divide the problem into several modules and to exchange their results in a bidirectional way. Secondly, we developed an application that integrates these modules. Finally, we designed a visualization application capable of providing data adapted to the needs. Finally, we tested the application with several participants in order to assess the effectiveness of our method. We obtained a score of 85.3 in terms of usability according to our SUS questionnaire

Large-scale simulations manager tool for OMNeT ++: expediting simulations and post-processing analysis

Usually, simulations are the first approach to evaluate wireless and mobile networks due to the difficulties involved in deploying real test scenarios. Working with simulations, testing, and validating the target network model often requires a large number of simulation runs. Consequently, there are a significant amount of outcomes to be analyzed to finally plot results. One of the most extensively used simulators for wireless and mobile networks is OMNeT++. This simulation environment provides useful tools to automate the execution of simulation campaigns, yet single-scenario simulations are also supported where the assignation of resources (i.e., CPUs) has to be declared manually. However, conducting a large number of simulations is still cumbersome and can be improved to make it easier, faster, and more comfortable to analyze. In this work, we propose a large-scale simulations framework called simulations manager for OMNeT ++ (SMO). SMO allows OMNeT++ users to quickly and easily execute large-scale network simulations, hiding the tedious process of conducting big simulation campaigns. Our framework automates simulations executions, resources assignment, and post-simulation data analysis through the use of Python’s wide established statistical analysis tools. Besides, our tool is flexible and easy to adapt to many different network scenarios. Our framework is accompanied by a command-line environment allowing a fast and easy manipulation that allows users to significantly reduce the total processing time to carry out large sets of simulations about 25% of the original time. Our code and its documentation are publicly available at GitHub and on our website.This work was supported by the Spanish Government through the Research Project sMArt Grid Using Open Source Intelligence (MAGOS) under Grant TEC2017-84197-C4-3-R. The work of Pablo Andrés Barbecho Bautista was supported by a grant from the Secretaría Nacional de Educación Superior, Ciencia y Tecnología (SENESCYT). The work of Leticia Lemus CÁrdenas was supported by a Ph.D. grant from the Academic Coordination of the University of Guadalajara, Mexico.Peer ReviewedPostprint (published version