Human Localization Sensor Ontology: Enabling OWL 2 DL Based Search for User's Location Aware Sensors in the IoT
The Semantic Sensor Web paradigm offers to solve the problem of sensors discovery in the Internet of Things by annotating sensors with semantic descriptions using ontologies, enabling the search for sensors by meaning and related concepts. However, the search for user's location aware sensors, i.e. those can be used to find user's location context, requires substantial knowledge in sensor specifications, localization methods, human location context and their interrelations, besides many other challenges. In this paper, the ontology for Human Localization Sensor is proposed to facilitate the location aware sensor search. Knowledge in human location sensing modalities are analyzed for common characteristics and described as defined classes for automatic sensor classification in OWL 2 DL. The ontology provides constructs to describe sensors and localization methods in relation to user's location context. It is aligned with major ontologies like Semantic Sensor Network, DOLCE Ultra Lite, QUDT, and GeoNames to increase expressivity as Linked Data. It also incorporates IoT aspects and extensible knowledge structure into the design. The evaluation based on a human sensing taxonomy survey shows that the ontology can be used to correctly perform semantic search for major human localization sensors.
2016 IEEE Tenth International Conference on Semantic Computing (ICSC)