Introducing Hierarchical Clustering with Real Time Stream Reasoning into Semantic-Enabled IoT
Jingyu Sun, Masato Kamiya, Susumu Takeuchi · 2018
Today, most of the IoT platforms including the standardizations for them are encountering a new stage called "Semantic Interoperability" which is expected to promise the intensive analytics and high level cross-domain intelligence in future. Although multiple IoT platforms such as oneM2M and OPC UA developed the outline architectures for IoT semantic implementation, the large cost on manual semantic annotation, ontology construction and metadata transferring hinder the practical use of these architectures. Efficient data aggression and computing perception for automatic semantic annotation and ontology learning become urgent for solving this problem. This paper did an exploratory investigation into these present researches and the future of semantic-enabled IoT. Real time stream reasoning technology and hierarchical clustering technology for automatic semantic annotation and reducing the semantic meta-data traffic are analyzed. The potential ways these two kinds of technologies can be combined and the possible benefits are estimated. A software architecture for facilitating these technologies in IoT is designed with introducing the respectively analyzed related algorithms and methodologies.