Time locality: A novel parameter for quality of context

Context awareness is a key technology in pervasive computing. Quality of context (QoC) is a compound measurement to rate the potential correctness of a context value. The different physical sensors aggregated to infer a context, naturally have different update frequencies. However the only time related QoC parameter is “up-to-dateness”, which refers to the time passed since the value was collected. We propose to add “time locality” as new QoC parameter to express the time difference between samples from different sensors, that are aggregated into one context. Theoretically and for the concrete example of human movement detection we show the improved accuracy achieved by the addition of this QoC parameter.