Energy-Efficient Algorithm for Mixed-Criticality Systems in E-Learning Environment

Low-energy consumption is a vital concern in E-learning due to high-volume processing and the fact that mobile technologies are usually battery-operated devices.

The method is simulated by developing a discrete-event simulation in C#. The validation of the proposed method is performed on generated task sets as used in similar work. The characteristic of randomly produced tasks is similar to the well-known techniques of task generation in mixed-criticality (MC) systems.

The simulation results show that energy consumption can be improved up to 23% in comparison to similar approaches. The most important factor for this satisfaction was the reservation times of critical tasks to further reduce the processor frequency.

The internet of thing (IoT) is poised to be one of the most disruptive technologies in E-learning environment. The IoT is a kind of MC system that integrates multiple things with different criticalities into the same platform. Mobile technologies provide education to people through mobile devices. These devices are usually battery-operated and owing to high-volume processing, Low-energy consumption becomes a vital concern in E-learning. Therefore, this paper was discussed about the MC system in general. Finally, the paper was proposed a scheduling technique to minimize the energy consumption of E-learning devices that use the IoT.