Scalable Processing of Context Information with COSMOS

Acceptance rate: 30% (24/80).International audienceUbiquitous computing environments are characterised by a high number of heterogeneous devices that generate a huge amount of context data. These data are used, for example, to adapt applications to changing execution contexts. However, legacy frameworks fail to process context information in a scalable and efficient manner. In this paper, we propose to organise the classical functionalities of a context manager to introduce a 3-steps cycle of data collection, data interpretation, and situation identification. We propose the COSMOS framework for processing context information in a scalable manner. This framework is based on the concepts of \emph{context node} and \emph{context management policies} translated into software components in software architecture. This paper presents COSMOS and evaluates its efficiency throughout the example of the composition of context information to implement a \emph{caching/off-loading} adaptation situation