Type of course:
Digital learning, Path
Language:
EN
Duration:
1 hour, 30 minutes
Workload:
6 hours
Proficiency:
Intermediate
Target:
Professionals, Students
This course is officially recognised and labelled by the European Institute of Innovation and Technology (EIT). EIT Label is a quality mark awarded to programmes demonstrating outstanding innovation, educational excellence and societal impact.
The Learning Path “Node-Red as a local IIoT Platform for machine interoperability” focusses on the Node-RED platform as a means to reach a higher level of connectivity and interoperability between machines. Its strengths and weaknesses will be noted, and the basic tools and knowledge needed to use Node-RED will be presented. After the basic functionalities and tools are presented, ways to use Node-RED with a variety of commonly available industrial communication protocols will be explored. Its capabilities of creating standardized databases will also be explored. Finally, Node-RED’s tools for creating dashboards for data monitoring will be presented together with an example.
Learning outcomes
- At the end of this LP the learner is able to recognize the different components and levels of a machine level IoT network as well as define their specific functions.
- At the end of this LP the learner is able to develop a communication gateway and database using an open-source tool (namely NodeRed) in order to create a local network by creating a personalized solution to a specific, one-off problem.
- At the end of this LP the learner is able to create simple remote dashboards for data visualization using the Node-RED software for backend and frontend coding as well as a managed database. The learner will be skillful enough to create not only utilitarian interfaces but intuitive and flexible dashboards capable of being adapted to a variety of situations and remote monitoring needs.
LessonNode-Red: Industrial Connectivity
Course Content
LessonNode-Red: Monitoring Dashboards
Course Content
LessonNode-Red as a Local IIoT Platform: Assessment Quiz
Course Content
Topics
Digital Transformation, Programming, Data mining