A dynamic and context-aware semantic mediation service for discovering and fusion of heterogeneous sensor data

Sensors play an increasingly critical role in capturing and distributing observations of phenomena in our environment. The vision of the semantic sensor web is to enable the interoperability of various applications that use sensor data provided by semantically heterogeneous sensor services. However, several challenges still need to be addressed to achieve this vision. More particularly, mechanisms that can support context-aware semantic mapping and that can adapt to the dynamic metadata of sensors are required. Semantic mapping for the sensor web is required to support sensor data fusion, sensor data discovery and retrieval, and automatic semantic annotation, to name only a few tasks. This paper presents a context-aware ontology-based semantic mediation service for heterogeneous sensor services. The semantic mediation service is context-aware and dynamic because it takes into account the real-time variability of thematic, spatial, and temporal elements that describe sensor data in different contexts. The semantic mediation service integrates rule-based reasoning to support the resolution of semantic heterogeneities. An application scenario is presented showing how the semantic mediation service can improve sensor data interpretation, reuse, and sharing in static and dynamic settings

A dynamic and context-aware semantic mediation service for discovering and fusion of heterogeneous sensor data

Sensors play an increasingly critical role in capturing and distributing observation of phenomena in our environment. The Semantic Sensor Web enables interoperability to support various applications that use data made available by semantically heterogeneous sensor services. However, several challenges still need to be addressed to achieve this vision. More particularly, mechanisms that can support context-aware semantic mapping that adapts to dynamic metadata of sensors are required. Semantic mapping for Sensor Web is required to support sensor data fusion, sensor data discovery and retrieval, and automatic semantic annotation, to name only a few applications. This paper presents a context-aware ontology-based semantic mediation service for heterogeneous sensor services. The semantic mediation service is context-aware and dynamic because it takes into account the real-time variability of thematic, spatial and temporal features that describe sensor data in different contexts. The semantic mediation service integrates rule-based reasoning to support resolution of semantic heterogeneities. An application scenario is presented showing how the semantic mediation service can improve sensor data interpretation, reuse, and sharing in static and dynamic settings