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Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long(More)
Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long(More)
As a variety of pervasive environments emerge, context-aware applications shall have to interact well with each of them. In this position paper, we propose extensions to the Strathclyde Context Infrastructure that gives context-aware applications the potential to adapt to such environments transparently. We present a vision of a context discovery technique(More)
Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Situation identification provides essential context information used by applications to adapt their behaviours.Current approaches to situation determination can be broadly categorised as(More)
Determining the situation within an environment is a key goal of smart environment research. A significant challenge in situation determination is reasoning about open-ended groups of people and devices that a smart environment may contain. Contemporary solutions are often tailored to the specific environment. In this position paper, we present a novel(More)