Contextual triple inference using a semantic reasoner rule to reduce the weight of semantically annotated data on fail–safe gateway for WSN

Abstract

The Internet of Things (IoT) combines miscellaneous technologies, which make it more diverse and applicable to different domains than a single technology. Semantic web technologies combined with IoT facilitate ubiquitous computing through machine-to-machine communication and semantic data management. Reusable domain ontologies, which provide a common semantic description for resources, are potential candidates for resolving the interoperability problem. The semantic annotation of sensor data using ontologies includes metadata and other thematic information regarding the data in the form of triples, on which reasoning can be performed to infer knowledge. The semantically annotated data are bulkier than the original data because of thematic metadata, and IoT devices have constrained resources to send this annotated data through a network. To reduce the weight of the annotated sensor data on networks, we established semantic data management by using semantic reasoner rules to reduce the number of triples from the semantic sensor data employing the unambiguous latent context information of a triple term. The triples can again be derived on the server instead of carrying the extra payload. A semantic rule was applied to the Jena semantic reasoner engine to reduce the triple on the annotated data. Furthermore, we developed a method for WSN fail–safe gateway on Zigbee mesh network that sends the semantically annotated sensor data through networks.