• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.1-9
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• 2024

This paper proposes an integration of the ED-H scheduling algorithm, known for optimal real-time scheduling, with the LoRaEnergySim simulator. This integration facilitates the simulation of interactions between real-time scheduling algorithms for tasks with time constraints in Class-A LoRaWAN Class-A devices using a super-capacitor-based energy harvesting system. The time and energy characteristics of LoRaWAN status and state transitions are extracted in a log format, and the task model is structured to suit the time-slot-based ED-H scheduling algorithm. The algorithm is extended to perform tasks while satisfying time constraints based on CPU executions. To evaluate the proposed approach, the ED-H scheduling algorithm is executed on a set of tasks with varying time and energy characteristics and CPU occupancy rates ranging from 10% to 90%, under the same conditions as the LoRaEnergySim simulation results for packet transmission and reception. The experimental results confirmed the applicability of co-simulation by demonstrating that tasks are prioritized based on urgency without depleting the supercapacitor's energy to satisfy time constraints, depending on the scheduling algorithm.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.17-27
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• 2022

In this paper, ED-H algorithm, an optimal real-time scheduling algorithm dealing with the characteristics of the integrated energy harvester system with a capacitor, is extended to satisfy the time constraint under the blackout state which is a deliberate power-off state by an intelligent power management unit adopted in the system. If the power supply system does not have enough energy, it temporarily shuts off the power supply to protect the circuit and capacitor and resumes the supply again when the capacitor is fully charged, which may delay the task execution during these blackout states by calculating the time according to the occurrence of the events. To mitigate the problem, even if task execution is delayed by the original ED-H algorithm, the remaining time of the subsequent time units no longer can afford to delay the execution of the task is predicted in the extended algorithm and the task is forced to be scheduled to meet the time deadline. According to the simulation results, it is confirmed that the algorithm proposed in this paper has a high scheduling performance increase of 0.4% to 7.7% depending on the characteristics of the set of tasks compared to the ED-H.