Scalable Management of Trajectories and Context Model Descriptions

Abstract

The ongoing proliferation of sensing technologies constitutes a huge potential for context-aware computing. It allows selecting relevant information about our physical environment from different sources and providers all over the globe. A fundamental challenge is how to provide efficient access to these immense amounts of distributed dynamic context information -particularly due to the mobility of devices and other entities. To enable such access to current and past position information about moving objects, we propose a family of protocols (CDR, GRTS) for efficiently tracking a moving objects trajectory at some remote database in real-time as well as a distributed indexing scheme (DTI) for optimized access to trajectory data that is partitioned in space to multiple database servers. For discovering context information that is relevant for the situation of an application, we propose a powerful formalism for describing context models in a concise manner and a tailored multidimensional data structure (SDC-Tree) for retrieving relevant context models out of potentially millions of descriptions.