A system for semantic data fusion in sensor networks

Emerging sensor network technologies are expected to substantially augment applications such as environmental monitoring, health-care, and home/commercial automation. However, much of the existing work focuses mainly on collecting and using sensor level data from isolated sensor networks directly, which still burdens applications with the task of interpreting the context and meaning of sensor data. In order to infer high-level phenomena, sensor data needs to be filtered, aggregated, correlated, and translated from many heterogeneous and dispersed sensor networks. In this paper, we present a novel system for decoupling the process of semantic data fusion from application logic based on semantic Content-based Publish/Subscribe techniques. Our main contribution is an integrated system that allows efficient semantic event detection to occur both within and across sensor networks by translating events using ontologies.