Design of Low-Cost Remote Labs using IoT-based Retrofitting

Abstract

Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere in the world. This has proved to be particularly important during the recent COVID-19 pandemic, where remote access has become essential. However, most remote labs are costly and need more flexibility for institutions to replicate experiments. This becomes a significant concern when trying to scale up to accommodate more students in developing countries. The work presented in this thesis primarily revolves around building hardware for a proposed end-toend remote lab system (RLabs). This is presented in two parts: one is retrofitting Internet of Things (IoT) components to laboratory experiments, and the second is adopting Computer-Vision (CV) techniques in a remote lab experiment. The RLabs platform includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at low cost by retrofitting IoT components. The proposed solution is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the student’s learning and the platform’s usability. Additionally, CV techniques are explored and used for a use case experiment of Conservation of Mechanical Energy to improve the accuracy and reduce the dependence on sensors used for remote experimentation. These CV techniques track the velocity of an object in the experiment, which is otherwise found out from infrared (IR) sensors. Later, velocities calculated from the CV technique are compared with the traditional IR sensors. The calculated velocities are further made accurate by employing linear regression.