Automatic Clustering and Semantic Annotation for Dynamic IoT Sensor Data

Abstract

In a dynamic IoT environment, distributed sensors are used to collect real-time data continually. However, it is difficult to transform the dynamic data into a machine-readable and machine-interpretable form. we propose a semantic annotation approach to annotate sensor data via semantics. Firstly, this approach builds an ontology based on Semantic Sensor Network Ontology (SSN Ontology) for dynamic IoT sensor data. Then, the new knowledge is collected from input data by using the K-Means clustering, and to update the semantic information into the base ontology. The updated ontology forms the basis for semantic annotation.