Abstract
The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices,control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantlychanged the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoTapplication development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations.However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. Theycannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. Forthis purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks.However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. Theinteroperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for anindustrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient useof SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper wepropose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema.orgsemantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machinebuilder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer todesign and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the applicationdevelopment process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complexapplication development tasks such as skill matching between Recipes and existing things can be automated. We will present theapproach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitativeand qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. Theresults of the evaluation are presented and discussed in the paper
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