Adaptation dynamique des services dans l'informatique ubiquitaire par utilisation de similarité contextuelle

Abstract

Pervasive computing or ubiquitous computing is a recent technology that has emerged from the convergence of several technologies: conventional computing where the PC is the central entity of information processing, distributed systems, telecommunications networks, electronics, embedded systems and finally internet. The main idea is that the technology can adapt to the preferences of the human while remaining transparent. The goal of ubiquitous or pervasive computing is the interconnection of all areas of life by enabling ubiquitous traffic data. The interaction and seamless adaptation to the environment with the user is based on the concept of context and the design of context-aware applications sensitive to the changes in the context. The context information is acquired from physical or virtual sensors. They are stored and modeled in a suitable form to the context model. They are then used by higher levels of abstraction to produce the requested actions adapted to the user. The adaptation of services provided to a user in a pervasive computing system or PCS is the purpose of our present work. In Pervasive computing, the adaptation of services is a dynamic process, where services are provided to a user in an individual manner in a reactive way, reacting to a change in the context or proactively by predicting a change of the context and reacts consequently. Our present work is essentially a step in this direction which consists in presenting an adaptation of services approach by exploiting the similarities between a current context and reference contexts using semantic similarity measures. The context will be defined and modeled and will be used as a factor: 1) for selection and improvement of services and 2) for prediction of future context to enable proactive adaptation of services.

Date	24 Nov 2016
Original language	French
Awarding Institution	École de technologie supérieure
Supervisor	Chakib Tadj (Supervisor) & Moeiz Miraoui (Co-supervisor)