Verification of uncertain context based on a theory of context spaces

Purpose: A challenging task for context-aware pervasive systems is reasoning about context in uncertain environments where sensors can be inaccurate or unreliable and inferred situations ambiguous and uncertain. This paper aims to address this grand challenge, with research in context awareness to provide feasible solutions by means of theoretical models, algorithms and reasoning approaches. Design/methodology/approach: This paper proposes a theoretical model about context and a set of context verification procedures, built over the model and implemented in a context reasoning engine prototype. The verification procedures utilize beneficial characteristics of spatial representation of context and also provide guidelines based on heuristics that lead to resolution of conflicts arising due to context uncertainty. The engine's reasoning process is presented and it is shown how the proposed modeling and verification approach contributes in tackling the uncertainty associated with the reasoning task. The paper experimentally evaluates this approach with a distributed simulation of a sensor-based office environment with unreliable and inaccurate sensors. Findings: Important features of the model are dynamic aspects of context, such as context trajectory and stability of a pervasive system in given context. These can also be used for context verification as well as for context prediction. The model strength is also in its generality and its ability to model a variety of context-aware scenarios comprising different types of information. Originality/value: The paper describes a theoretical model for context and shows it is useful not only for context representation but also for developing reasoning and verification techniques for uncertain context.