Multi-user Preference Model and Service Provision in a Smart Home Environment

An important issue to be addressed in a smart home environment is how to provide appropriate services according to the preference of inhabitants. In this paper, we aim at developing a system to learn a multiple users´ preference model that represents relationships among users as well as dependency between services and sensor observations. Thus, the service can be inferred based on the learnt model. To achieve this, we propose a three-layer model in our work. At the first layer, raw data from sensors are interpreted as context information after noise removal. The second layer is dynamic Bayesian networks which model the observation sequences including inhabitants´ location and electrical appliance (EA) information. At the highest layer, we integrate second layer´s, environment information and the relations between inhabitants to provide the service to inhabitants. Therefore, the system can infer appropriate services to inhabitants at right time and right place, and let them feel comfortable. In experiments, we show our model can provide reliable and appreciate services to inhabitants in a smart home environment.