Pervasive systems must offer an open, extensible, and evolving portfolio of services which integrate sensor data from a diverse range of sources. The core challenge is to provide appropriate and consistent adaptive behaviours for these services in the face of huge volumes of sensor data exhibiting varying degrees of precision, accuracy and dynamism. Situation identification is an enabling technology that resolves noisy sensor data and abstracts it into higher-level concepts that are interesting to applications. 

In this talk, I will provide a comprehensive analysis of the nature and characteristics of situations, discuss the complexities of situation identification, and introduce a novel situation identification technique called “context lattice”. The context lattice is built on a sound mathematical model, aiming to identify situations by systematically exploring the semantics of sensor data, domain knowledge, and situations in a pervasive computing system. I will present and discuss the evaluation results when applying this technique to recognising human activities in smart home environments. This talk will be concluded with challenging questions in the area of situation identification.
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