Nonparametric Discovery of Contexts and Preferences in Smart Home Environments

With the popularity of Internet of Things, lots of resource constrained devices equipped with sensors and actuators are pervasively deployed to compose a smart environment, and Big Data are obtainable for a system to do further analytics thus to achieve human-centric purposes. One such human-centric system is a smart home which analyze Big Data to recognize contexts and their corresponding preferences for service configuration thus to provide context-aware services. However, since these Big Data are generated in real-time with huge amount, analytics based on conventional supervised way is not desirable due to the requirement of human efforts. In addition, there are usually multiple inhabitants with multiple combination of contexts in a home environment, and it is difficult to fully collect all these possible context combination as well as their corresponding preferences in advance. Therefore, this paper proposes an unsupervised nonparametric analytics method with a framework for human-centric smart homes to automatically discover contexts and their corresponding service configurations, and the models resulting from the proposed analytics can also be used to determine the preference for a context combination unseen before.