  Explainability is crucial for complex systems like pervasive smart
environments, as they collect and analyze data from various sensors, follow
multiple rules, and control different devices resulting in behavior that is not
trivial and, thus, should be explained to the users. The current approaches,
however, offer flat, static, and algorithm-focused explanations. User-centric
explanations, on the other hand, consider the recipient and context, providing
personalized and context-aware explanations. To address this gap, we propose an
approach to incorporate user-centric explanations into smart environments. We
introduce a conceptual model and a reference architecture for characterizing
and generating such explanations. Our work is the first technical solution for
generating context-aware and granular explanations in smart environments. Our
architecture implementation demonstrates the feasibility of our approach
through various scenarios.
