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

Context information such as environmental and physiological data is considered as a type of knowledge whose attributes can be defined in the form of ontology. Therefore, reasoning and inferring may be carried out on the context knowledge. In this research work, we introduce a model that takes the dynamic nature of a context-aware system into consideration. This model is constructed according to the 4D-objects approach and 3D-events for the data collected from a WBAN. In order to support mobility and reasoning on the temporal data transmitted from WBAN, an ontology-based hierarchical model is presented. It supports the relationship between heterogeneous environments and reasoning on the context data for extracting a higher-level knowledge. Location is considered as a temporal attribute. In order to support temporal entity, the reification method and Allen’s algebra relations are used. Using reification, new classes of the time_slice and time_interval, and new attributes of ts_time_slice and ts_time_intervalare defined in the context-aware ontology. Then thirteen logic relations of Allen such as Equal, After, and Before are added by the OWL-Time ontology to the properties. Integration and consistency of the 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. In terms of completeness, adaptability, conciseness, consistency, computational efficiency, and clarity metrics, the overall performance quality is 0.9137.