A semantic processing framework for IoT-enabled communication systems

Enterprise Collaboration Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things can play a crucial role in this process, but is far from being seamlessly integrated in modern online communications. In this paper, we showcase the use of a solution that goes beyond today’s ad-hoc integration and processing of heterogeneous data sources for static and streaming data, providing more flexible and efficient processing techniques that can bridge the gap between IoT and online Enterprise Communication Systems. We document the technologies used for sensor deployment, sensor data acquisition based on the OpenIoT framework, and stream federation. Our main contributions are the following, i) we present a conceptual architecture of IoT-enabled Communication Systems, that builds upon existing frameworks for semantic data acquisition, and tools to enable continuous processing, discovery and federation of dynamic data sources based on Linked Data; ii) we present a semantic information model for representing and linking IoT data, social data and personal data by re-using and extending the existing standard semantic models; iii) we evaluate the performance of virtualisation of IoT sources based on OpenIoT in our testbed and show the impact of transmission, annotation and data storage, as well as initial results on scalability of RDF stream query processing in such a framework, providing guidelines and directions for optimisation.