Automated context learning in ubiquitous computing environments

Abstract

Context awareness enables services and applications to adapt their behaviour to the current situation for the benefit of their users. It is considered as a key technology within the IT industry, for its potential to provide a significant competitive advantage to services providers and to give subtantial differentiation among existing services. Automated learning of contexts will improve the efficiency of Context Aware Services (CAS) development. In this paper we present a system which supports storing, analyzing and exploiting an history of sensors and equipments data collected over time, using data mining techniques and tools. This approach allows us to identify parameters (context dimensions), that are relevant to adapt a service, to identify contexts that needs to be distinguished, and finally to identify adaptation models for CAS such as the one which would automatically switch off/on of lights when needed. In this paper, we introduce our approach and describe the architecture of our system which implements this approach. We then presents the results obtained when applied on a simple but realistic scenario of a person moving around in her flat. For instance the corresponding dataset has been produced by devices such as white goods equipment, lights and mobile terminal based sensors which we can retrieve the location, position and posture of its owner from. The method is able to detect recurring patterns. For instance, all patterns found were relevant for automating the control (switching on/off) of the light in the room the person is located. We discuss further these results, position our work with respect to work done elsewhere and conclude with some perspectives.