Mobile learning evolution and emerging computing paradigms: An edge-based cloud architecture for reduced latencies and quick response time

Abstract

The emerging computing paradigms like mobile cloud, mobile edge computing, fog computing, AI, and 5G provide the scope to advance mobile learning (m-learning) performances across educational disciplines. Edge computing brings the computing power at the edge of the network and closer to mobile learning actors. This study investigates the potentials of emerging computing paradigms and proposes a novel mobile edge-based hierarchical architecture to optimize m-learning performance efficiency. The architecture's design follows the ETSI MEC ISG framework and ISG MEC technical requirements. An m-learning use case was executed to evaluate the architecture's performance efficiency in a university facility. The implementation results show reduced latencies and quick response time. The architecture handles data locally on-site, lowers demand on radio access bandwidth, increases confidential data privacy, and continues the application execution even the network is disrupted. The architecture is open to redesign, reconfigure, and integrate with other computing paradigms. IoT-based m-learning use cases should be considered by reflecting learners' scaling of resource needs. Execution of complex use cases will expand the architecture base, MEC-based learning models usage, and change learning-teaching dynamics across educational disciplines.

Keywords

• Emerging computing paradigms
• IoT
• Mobile cloud computing
• Mobile edge computing
• Mobile learning applications
• Mobile learning architectures