Energy-Efficient Computation Offloading With DVFS Using Deep Reinforcement Learning for Time-Critical IoT Applications in Edge Computing
Why this work is in the frame
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Bibliographic record
Abstract
Internet of Things (IoT) is a technology that allows ordinary physical devices to collect, process, and share data with other physical devices and systems over the Internet. It provides pervasively connected infrastructures to support innovative applications and services that can automate otherwise intensely laborious manual effort. Edge computing (EC) complements the powerful centralized cloud servers by providing powerful computation capability close to the data source, minimizing communication latency, and securing data privacy. The energy consumption problem has continued to receive much attention from the IoT community in applying various techniques to reduce energy consumption while still meeting the computational demand. In this article, we propose an application-deadline-aware data offloading scheme using deep reinforcement learning and dynamic voltage and frequency scaling (DVFS) in an EC environment to reduce the energy consumption of IoT devices. The proposed scheme learns the optimal data distribution policies and local computation DVFS frequency scaling by interacting with the system environment and learning the behavior of the device, network, and edge servers. The proposed scheme was tested on multiple EC environments with different IoT devices. Experimental results show that this scheme can reduce energy consumption while achieving the IoT application and services timing and computational goals. The proposed scheme has substantial energy savings when compared with the native Linux governors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it