Thanks to the recent rapid technological advancement in IoT usage, there is a need for students to learn IoT-based concepts using a dedicated experimental platform. Furthermore, being forced into remote learning due to the ongoing COVID-19 pandemic, there is an urgent need for innovative learning methods. From our perspective, a learning platform should be reconfigurable to accommodate multiple applications and remotely accessible at any time, from anywhere, and on any connected device. Considering that many of the university courses are now held online, the reliability and scalability of the system become critical. This paper presents the design and development of a wireless configurable myRIO-based sensor node that connects to SystemLink Cloud. The sensors that were used are for ambient light, temperature, and proximity. A graphical programming environment (G-LabVIEW) and related APIs were used for rapid concept-to-development process. Distinct applications have been developed for the instructor and students, respectively. The students can select which sensor and application to run on the system and observe the measurements on the local student's application or the cloud platform at a specific moment. They can also read the data on the cloud platform and use them in their LabVIEW application. In the context of remote education, we strongly believe that this platform is and will be suitable for the COVID and Post-COVID eras as well because it creates a much better remote laboratory experience for students. In conclusion, the system that was developed is innovative because it is software reconfigurable from the device, from the instructor's application and cloud via a web browser, it is intuitive, and it has a user-friendly interface. It meets most of the necessary requirements in the current era, being also highly available and scalable in the cloud.
Keywords: SystemLink Cloud; cloud; myRIO; pmod sensors; reconfigurable; tags.