Energy-Efficient Resource Allocation for D2D-Assisted Fog Computing
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we address the problem of energy-efficient resource allocation in a multi-device D2D-assisted fog computing scenario, where the goal is to minimize the total energy consumption subject to constraints on the transmit powers, computation resources and task processing times. The considered problem is non-convex and finding its global optimum is generally intractable; hence we propose two sub-optimal approaches to solve it. First, by investigating the relationship between the task processing time and the total energy consumption, we show how the original problem can be relaxed into a sequence of convex subproblems whose solutions can be efficiently obtained via standard algorithms. Second, to further reduce computational complexity, we propose a low-complexity heuristic resource allocation strategy which does not require calculating gradients and the Hessian matrices in the solution process. We also develop a lower bound on the total energy consumption for the considered task offloading scenario as a benchmark for comparison purpose. Computer simulations under a wide range of conditions and parameter settings show that both methods achieve a near-optimal solution in comparison to the lower bound.
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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 | 0.001 |
| Science and technology studies | 0.004 | 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