Expanding Support for Engaged Remote Student Learning of Internet of Things Concepts and Technology

Abstract Internet of Things (IoT) based systems have proven to be effective solutions in a wide variety of application areas. The availability of low-cost versions of the hardware elements that form the basis of these systems, including processor boards, sensors, and communication devices, combined with expanding software support, such as cloud based IoT resources, ensure the popularity of IoT approaches will continue to expand further. Consequently, the inclusion of IoT concepts and technologies is becoming an increasingly important part of many engineering degree programs (e.g., computer engineering, electrical engineering, computer science, and mechanical engineering). An ongoing project at Texas A&M University-Kingsville has focused on expanding the coverage of IoT concepts and technology that is included in engineering degree programs. A particular emphasis of the project has been on the support of remote engaged student learning. To ensure students are given not only theoretical coverage of IoT concepts, but also receive practical, hands-on experience, a toolkit approach has been utilized. Remotely learning students, who do not have easy access to a classroom laboratory setting, are provided with an IoT toolkit they can utilize throughout the semester so they will be able to perform IoT exercises and assignments in order to fully engage with the material being covered. The initial IoT toolkit utilized for the project was a very basic one that included a single board computer, sensors, actuators, LEDs, a breadboard, and jumper wires. More recently a more advanced toolkit has been assembled based on a commercially available IoT learning platform. A series of exercises have been developed to accommodate the learning toolkits and facilitate remotely learning students. As reported previously, a set of five introductory level exercises were originally developed and provided for students utilizing the basic IoT toolkit to assist them in becoming familiar with its components and basic IoT concepts. The introductory exercises were later adapted into a form that is appropriate for the advanced IoT toolkit. This paper reports on an additional set of exercises that have been developed more recently to teach more advanced IoT concepts to students utilizing the expanded features and capabilities of the advanced IoT toolkit. While the examples and solutions developed for the first two sets of basic exercises were written in the C language, the support materials created for the more advanced exercises utilize the Python language in order to accommodate a wider variety of student backgrounds.