A Mixed Fog/Edge/AIoT/Robotics Education Approach based on Tripled Learning
The article describes a triple learning approach to educational process using a specialized three level training robotics platform to train professionals in the higher education system, which includes software and hardware for the sequential study of general intelligence related AI, such as Internet of Things, Cloud Computing, Robotics, and more. The described solution is based on Fog/Edge/AIoT stack and allows to build both set of laboratory works and complex completed projects in the context of the concept of tripled learning, as well as to carry out research projects. The platform described is modular and contains several levels of difficulty for optimal configuration according to the course syllabus. The usage of an Artificial Intelligence approach with remote data preprocessing (at edge level) can significantly reduce the computing and time resources and complexity, as well as refuse centralized storage of the entire array of data, which brings the processing of data much closer to online mode. The proposed platform allows improving the quality of the educational process, to increase its orientation to practical experience and to reflect the current state of AI synergy through Fog/Edg/Cloud with IoT.
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