Situation awareness meets ontologies: A context spaces case study

Abstract

Efficiency and appeal of pervasive computing systems strongly depends on how well and robustly they represent and reason about context and situations. Populating situation search space and inferring situations from context which, in turn, is computed from fusing sensor data and observations remains a major research challenge. This paper proposes to use ontologies as representation of domain knowledge to generate situation search space and then match context with already defined situations. To illustrate the feasibility, a context spaces approach is used to represent, generate and reason about situations as abstractions in a multidimensional space. The proposed approach is evaluated and discussed.