Remote Laboratory for developing an IoT system

The rapid evolution of Industry 4.0, driven by the Internet of Things (IoT), has revolutionized education, necessitating innovative platforms for the practical training of trainees. On the other hand, Remote Laboratories (RLs) have proven their worth, offering a safe and controlled environment for users to work in from the comfort of their working space at any time. This paper attempts to address two research questions: (1) What structure should a remote laboratory designed for IoT education have? and (2) Can Machine Learning (ML) assist the learning procedure in evaluating the programming code submitted by trainees? A literature review was conducted to answer the first question, highlighting the essential components of IoT education, including the development and programming of low-cost, open-source microcontrollers like Arduino, the development and programming of flexible server solutions like those based on Node-RED, and efficient Machine-to-Machine (M2M) protocols, such as MQTT, for client-server communication. The review also underscored the growing demand for hands-on, remote learning environments to enhance IoT skill acquisition. In this context, we developed the RLP-HMU2, an upgraded remote laboratory platform for teaching IoT application development. RLP-HMU2 employs a unique two-step process where students program Arduino microcontrollers and develop Node-RED applications, with continuously monitored progress. To address the second question, this platform integrates machine learning for automated code evaluation, providing personalized feedback and learning analytics via an xAPI framework. The ML module, implemented using decision tree algorithms, assesses student code accuracy and adaptively improves its classification model based on new submissions. Our findings demonstrate that this approach fosters practical IoT skills while empowering educators with tools for tailored instruction. This work represents a significant step toward scalable, effective IoT education by combining structured curriculum design, advanced remote labs, and innovative assessment technologies.