Context-awareness refers to the idea that applications adapt to their context of use including, for example, location, nearby devices and user habits. In the last years, billions of sensors have been deployed all over the globe, which allow creating comprehensive context models of our physical environment. The availability of such models constitutes a huge potential for context-aware computing as it allows selecting relevant context information from different providers all over the globe. However, such sharing of context information poses a number of challenges. A fundamental problem is how to provide efficient access to the immense amounts of distributed dynamic context information - particularly due to the mobility of devices and other entities. To enable efficient access to current and past position information about moving objects, we propose a family of trajectory tracking protocols (CDR, GRTS) as well as a distributed indexing scheme (DTI) for trajectories. Given a certain accuracy bound, CDR and GRTS optimize the storage consumption and communication cost for tracking a moving object's trajectory in real-time at some remote database and allow for various trade-offs between computational costs, reduction efficiency, and communication. DTI enables efficient access to trajectory information that is partitioned in space and stored by different servers for scalability reasons. In addition, an extended scheme DTI+S is presented, which optimizes the processing of aggregate queries. 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 corresponding index structure (SDC-Tree). The formalism considerably extends existing approaches for describing information sources by constraints and permits to adjust between different semantics for matching descriptions against corresponding queries. The SDC-Tree enables to discover relevant context models out of potentially millions of descriptions efficiently using multidimensional indexing capabilities.