A lattice-theoretic approach to runtime property detection for pervasive context

Runtime detection of contextual properties is one of the primary approaches to enabling context-awareness. Existing property detection schemes implicitly assume that contexts under detection belong to the same snapshot of time. However, this assumption does not necessarily hold in the asynchronous pervasive computing environments. To cope with the asynchrony, we first model environment behavior based on logical time. One key notion of our model is that all meaningful observations of the environment have the lattice structure. Then we propose the LAT algorithm, which maintains the lattice of meaningful observations at runtime. We also propose the LATPD algorithm, which achieves detection of contextual properties at runtime. We implement algorithms over the open-source context-aware middleware MIPA, and simulations are conducted. The evaluation results show that LAT and LATPD support effective detection of contextual properties in asynchronous environments.