Pervasive environments are characterized by a large number of embedded devices offering their services to the user. Which of the available services are of most interest to the user considerably depends on the user's current context. User context is often rich and very dynamic; making an explicit, user-driven discovery of services impractical. Users in such environments would instead like to be continuously informed about services relevant to them. Implicit discovery requests triggered by changes in the context are therefore prevalent. This paper proposes a proactive service discovery approach for pervasive environments addressing these implicit requests. Services and user preferences are described by a formal context model called Hyperspace Analogue to Context, which effectively captures the dynamics of 296 World Wide Web (2011) 14:295-319 context and the relationship between services and context. Based on the model, we propose a set of algorithms that can continuously present the most relevant services to the user in response to changes of context, services or user preferences. Numeric coding methods are applied to improve the algorithms' performance. The algorithms are grounded in a context-driven service discovery system that automatically reacts to changes in the environment. New context sources and services can be dynamically integrated into the system. A client for smart phones continuously informs users about the discovery results. Experiments show, that the system can efficiently provide the user with continuous, up-to-date information about the most useful services in real time.

No abstract

Inhabitants of today's smarter homes struggle with complicated user interfaces and inflexible home configurations. The proposed smart home recommender system addresses these issues by continuously interpreting the user's current situation and recommending services that fit the user's habits, i.e. automate some action that the user would want to perform anyway. With these recommendations it is possible to build much simpler user interfaces that highlight the most interesting choices currently available. Configuration becomes much more flexible, since the recommender system automatically learns user habits. Evaluations on two smart home datasets show that the algorithm produces correct recommendations with 61% and 73% accuracy, respectively.

Home automation represents a growing market in the industrialized world. Today’s systems are mainly based on ad hoc and proprietary solutions, with little to no interoperability and smart integration. However, in a not so distant future, our homes will be equipped with many sensors, actuators and devices, which will collectively expose services, able to smartly interact and integrate, in order to offer complex services providing even richer functionalities. In this paper we present the approach and results of SM4ALL- Smart hoMes for All, a project investigating automatic service composition and advanced user interfaces applied to domotics

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.