An approach towards removal of data heterogeneity in SDN-based IoT framework

Abstract

IoT is an emerging technology that provides innovative solutions to our day-to-day life challenges. With billions of diverse smart objects, IoT produces gigantic amount of heterogeneous data that mostly need to be processed in real time and shared across systems and applications. For IoT data to be truly accessable and interpretable, the data heterogeneity problem needs to be resolved first. Semantic technologies is one of the most used solutions for data integration across systems. Data represented as knowledge graphs enables easier querying and matching. To address this, we propose an SDN based IoT architectural framework utilizing semantics for data integration and sharing using Kafka and Spark Streaming for real-time data handling. One main contribution of this work is that any consistent IoT ontology, defined using Web Ontology Language(OWL) which explicitly specify its classes, objects and data properties, can be used to convert the stream data into RDF format automatically using Natural Language Processing endeavor. SDN helps in dynamically locating the data producing devices such as sensors for providing flexible data routing. We have presented a testbed experimentation framework and evaluated the results with simulated virtual sensors. The homogeneous results from diverse datasets was generated in acceptable minimal time. A use-case for Air Quality Monitoring is given and SPARQL queries executed have shown satisfactory response time with over thousands of triples.