There is an inherent chasm between the real-world and the world that can be perceived by computer systems, yielding uncertainty and ambiguity in system perceived context, with consequent effect on the performance of context-aware systems. While the problem is complex in depth and breadth, we explore an approach where context is characterized at different levels of abn, and where contextual information at high-levels of abstraction and ontext at low-levels of abstraction can be used to validate and correct low-level sensed context such as location. We describe a randomly generated simulation of locations that might be sensed by a positioning technology, and how our approach can be used to validate and correct the sensed locations.
Amir Padovitz, Seng Wai Loke, Arkady B. Zaslavsky