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

Background: 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. Methods: 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. Results: 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. Conclusions: 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.