Evaluating Task Execution Performance Under Energy Measurement Overhead
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912533174026240 |
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| author | Ashraf, Mateen Jahanbazi, Shahab López, Onel L. A. |
| author_facet | Ashraf, Mateen Jahanbazi, Shahab López, Onel L. A. |
| contents | Energy-awareness for adapting task execution behavior can bring several benefits in terms of performance improvement in energy harvesting (EH) Internet of Things (IoT) devices. However, the energy measurement cost of acquiring energy information, which is traditionally ignored, can potentially neutralize or even reverse the potential benefits. This paper highlights operational parameters, such as energy measurement frequency and task execution frequency, which can be tuned to improve the task execution performance of an EH-IoT device. To this end, we consider energy-blind (EB) and energy-aware (EA) task decision approaches and compare their task completion rate performance. We show that, for specific hardware design parameters of an EH-IoT device, there exists an optimal energy measurement/task execution frequency that can maximize the task completion rate in both approaches. Moreover, if these parameters are not chosen appropriately, then energy measurement costs can cause EA scheduling to underperform compared to EB scheduling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Task Execution Performance Under Energy Measurement Overhead Ashraf, Mateen Jahanbazi, Shahab López, Onel L. A. Signal Processing Energy-awareness for adapting task execution behavior can bring several benefits in terms of performance improvement in energy harvesting (EH) Internet of Things (IoT) devices. However, the energy measurement cost of acquiring energy information, which is traditionally ignored, can potentially neutralize or even reverse the potential benefits. This paper highlights operational parameters, such as energy measurement frequency and task execution frequency, which can be tuned to improve the task execution performance of an EH-IoT device. To this end, we consider energy-blind (EB) and energy-aware (EA) task decision approaches and compare their task completion rate performance. We show that, for specific hardware design parameters of an EH-IoT device, there exists an optimal energy measurement/task execution frequency that can maximize the task completion rate in both approaches. Moreover, if these parameters are not chosen appropriately, then energy measurement costs can cause EA scheduling to underperform compared to EB scheduling. |
| title | Evaluating Task Execution Performance Under Energy Measurement Overhead |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2508.08757 |