Geospatial Sensor Network eLearning Collaboratory - A Portal for Sensor Knowledge Acquisition and Representation

Nowadays, sensors and sensor networks are being widely used both in the theoretical research and in the engineering application. In order to meet the standardization requirement, the OGC (Open Geospatial Consortium) SWE (Sensor Web Enablement) framework is extended to enable all types of sensors, instruments, and imaging devices to be accessible. However, the raw sensor data cannot represent a wealth of information especially semantic information, and cannot be easily recognized by computers either. With semantic Web proposed, formal definitions are captured in ontologies, making it possible for computers to interpret and relate data content more effectively. In the paper, we review the methods of knowledge acquisition and representation, propose to adopt semantic Web to acquire and represent sensor knowledge. We are designing and developing a Web portal named GSNC (Geospatial Sensor Network eLearning Collaboratory) under the SWE framework. The GSNC application combines sensor data with semantics identified by human and machines, and makes the sensor knowledge acquisition and representation available. In addition, the GSNC project provides the Web-based Sensor ML (Sensor Model Language) editor toolkit which can be migrated to other applications.