Environmental user-preference learning for smart homes: An autonomous approach

Abstract

The ubiquitous computing concept was introduced by Weiser in 1991. Despite the many advances since then, the use of ubiquitous spaces is not yet widespread in home environments, due to the difficulty in using context information and artificial intelligence algorithms. This work combines context-management architecture and machine-learning techniques to infer environmental user preferences automatically. Context information is represented with ontologies and the system is organized through a service-oriented architecture. The system learns all the parameters that the user configures in the home. These parameters are taken as the users' preferences and will be learned automatically by means of an algorithm based on the KNN machine learning algorithm. These preferences will enable the development of context-aware applications for the home environment that will proactively adjust, depending on the learned values and the context. These kinds of applications will further the concept of ambient-assisted living and, hence, aid in improving quality of life in the home.