The Design And Implement Of Resource Modeling Tools In The Internet Of Things

The Internet of Tings provides a mass of resources and information. The heterogeneity, instability, evolution and other characteristics of resources bring new challenges to information sharing. Generalized resources contain the resource devices in the lower layer and entities in the upper layer in IOT. It’s the essence of the resource modeling in IOT to model information in the standard and computer comprehensible way to realize the sharing and interoperability of resources in IOT.Ontology is widely employed in most of the existing works of the Internet of Things to describe the resources in IOT. The existing ontologies of resources in IOT can be managed by some ontology management tools. It is difficult for a person in the specific field to understand and operate the information in the form of ontology. There is no description about resources, entities and dynamic bindings between them. The conversion from sensing information in the lower layer to the more meaningful contextual information in the upper layer is unsupported in ontology. What’s more these tools support little for batch data manipulation and real-time applications.According to the background above, we propose a method of field-oriented modeling, based on which a resource modeling tool is designed and implemented. The tool employs the layered design architecture:Ontology is still employed in the lower layer to describe information standardly while information in the upper layer is presented by their levels and types. Different elements like resources, entities, properties and the instantiations of concepts are ultimately created through constructing resource template and resource instance. In this way, complicated concepts in ontology are shielded by providing users with information related to specific business and a visual environment for resource modeling. It is available to define bindings between the existing resources and entities. On this basis, property values of instances and instances can be bound and resource bind is realized, which enables the sensing raw data in the lower layer to be promoted to business-related physical object in the upper layer. In addition, the tool can support data operation on a large scale and the generated ontology can be converted to data saved in real-time database. After functional test and application test, the tool is verified to realize the function of resource modeling. Eventually we summarize the completed work and the shortages of the tool, and make a prospect for future work.