Decentralized Bandwidth Allocation Framework for Energy-Efficiency and Fog Integration in PONs
Why this work is in the frame
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Bibliographic record
Abstract
With the continuous growth of telecommunication networks, improving their energy efficiency to reduce the carbon footprint has become one of the most important research topics of today. In this paper, we focus on power conservation in passive optical networks (PONs), for which many centralized-based algorithms have been proposed in the literature. For the first time, we propose a novel energy-efficient framework that is decentralized-based. The proposed decentralized framework is designed to meet the requirements of next-generation access networks by addressing three main challenges; achieving high network performance, conserving energy, and supporting edge-to-edge communications for fog and edge computing. Numerical results show that, even though additional transceivers are used, the proposed framework achieves better network performance with lower energy consumption than its centralized counterpart.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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