Context-aware Modeling for Spatio-temporal Data Transmitted from a Wireless Body Sensor Network

Context-aware systems must be interoperable and work across different platforms at any time and in any place. Context data collected from wireless body area networks (WBAN) may be heterogeneous and imperfect, which makes their design and implementation difficult. In this research, we introduce a model which takes the dynamic nature of a context-aware system into consideration. This model is constructed according to the four-dimensional objects approach and three-dimensional events for the data collected from a WBAN. In order to support mobility and reasoning on temporal data transmitted from WBAN, a hierarchical model based on ontology is presented. It supports the relationship between heterogeneous environments and reasoning on the context data for extracting higher-level knowledge. Location is considered a temporal attribute. To support temporal entity, reification method and Allen’s algebra relations are used. Using reification, new classes Time_slice and Time_Interval and new attributes ts_time_slice and ts_time_Interval are defined in context-aware ontology. Then the thirteen logic relations of Allen such as Equal, After, Before is added by OWL-Time ontology to the properties. Integration and consistency of context-aware ontology are checked by the Pellet reasoner. This hybrid context-aware ontology is evaluated by three experts using the FOCA method based on the Goal-Question-Metrics (GQM) approach. This evaluation methodology diagnoses the ontology numerically and decreases the subjectivity and dependency on the evaluator’s experience. The overall performance quality according to completeness, adaptability, conciseness, consistency, computational efficiency and clarity metrics is 0.9137.