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Reconfigurable Wireless Sensor Node Remote Laboratory Platform with Cloud Connectivity.

Tinashe Chamunorwa1, Horia Alexandru Modran1, Doru Ursuțiu1,2

  • 1Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, 500036 Brasov, Romania.

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Summary

This study developed a reconfigurable, cloud-connected Internet of Things (IoT) experimental platform for remote STEM education. The system enhances the remote laboratory experience for students, offering accessibility and scalability.

Keywords:
SystemLink CloudcloudmyRIOpmod sensorsreconfigurabletags

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Area of Science:

  • Engineering Education
  • Internet of Things (IoT)
  • Cloud Computing

Background:

  • Rapid advancements in IoT necessitate dedicated experimental platforms for student learning.
  • The COVID-19 pandemic highlighted the urgent need for innovative remote learning solutions in higher education.
  • Existing remote learning platforms often lack reconfigurability, remote accessibility, reliability, and scalability.

Purpose of the Study:

  • To design and develop a wireless, reconfigurable, myRIO-based sensor node for IoT education.
  • To create a cloud-connected platform enabling remote access to sensor data and applications.
  • To enhance the remote laboratory experience for students in the context of online learning.

Main Methods:

  • Developed a wireless myRIO-based sensor node integrating ambient light, temperature, and proximity sensors.
  • Utilized a graphical programming environment (G-LabVIEW) and APIs for rapid development.
  • Integrated the system with SystemLink Cloud for remote accessibility and data management.

Main Results:

  • Created distinct instructor and student applications for system control and data observation.
  • Enabled students to select sensors and applications, viewing data locally or via the cloud.
  • Demonstrated software reconfigurability from the device, instructor application, and cloud interface.

Conclusions:

  • The developed platform offers an intuitive, user-friendly, and innovative solution for remote IoT education.
  • The system is highly available and scalable, suitable for both current and future remote learning needs.
  • This reconfigurable, cloud-based approach significantly improves the remote laboratory experience for students.