Scalable Processing of Context Information with COSMOS


Ubiquitous computing environments are characterised by a high number of heterogeneous devices that generate a huge amount of context data. These data are used 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, interpretation, and situation identification. We propose the COSMOS framework, which is based on the concepts of context node and 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 caching/off-loading adaptation situation.