Social-Aware Energy-Efficient Data Dissemination with D2D Communications
Bibliographic record
Abstract
With the high penetration rate of smart mobile devices, it is appealing to exploit device-to-device (D2D) communications for data dissemination, e.g., in disaster alerts and event notifications. The popularity of social networks also offers good opportunities to improve the efficiency of data dissemination. Though there have been some existing works on data dissemination with D2D and mobile social networks, many focus on mitigating the D2D co-channel interference to achieve high resource utilization. As mobile devices are power-limited, it is important to consider the energy efficiency and finishing time in data dissemination. In this paper, we aim at developing an effective solution for D2D data dissemination to balance between total energy consumption and transmission completion time. In particular, we propose novel algorithms for seed selection and transmission scheduling with a single seed or multiple seeds. Simulation results demonstrate that our solution outperforms two reference schemes in total energy consumption while achieving a good balance for transmission completion time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".