PROPOSING A HIERARCHICAL ARCHITECTURE FOR EFFICIENT MOBILE LEARNING AND ACCEPTABLE DELAY

Abstract

The emergence of new computing paradigms like mobile cloud, mobile edge computing, fog computing, artificial intelligence, and 5G opens up opportunities to enhance mobile learning outcomes across various subjects. By relocating processing capabilities to the network's edge, where mobile-learning agents can readily access them, this study explores the potential of these paradigms. A novel mobile computing hierarchical architecture is proposed to revolutionize mobile learning effectiveness. This architecture offers benefits such as reduced response times, minimized delays, and on-site data processing. This local data processing lessens the demand on radio access bandwidth, enhances data privacy, and enables uninterrupted app functionality even during network disruptions. This adaptable framework can be customized, reconfigured, and integrated with other computing approaches. While designing IoT-based mobile learning use cases, learner-specific resource requirements must be considered. Incorporating complex use cases will expand the architecture's foundation, boost the adoption of MEC-based learning models, and reshape the dynamics of education across disciplines.