A Coalitional Graph Game for Device-to-Device Data Dissemination with Power Budget Constraints
Bibliographic record
Abstract
With the evolution of wireless networks and pervasive mobile devices, device-to-device (D2D) communications have been envisioned as an effective means for data dissemination, e.g., in disaster alerts and event notifications. As mobile devices are battery-powered, it is essential to save power when scheduling D2D links for data dissemination. Also, users are generally more willing to forward data to others with social connections. In this paper, we take into account two important aspects, i.e., D2D users' social incentive constraint and power budget constraint, to enable more practical data dissemination. It is found that it is very difficult to obtain an optimal solution that minimizes the total power consumption while satisfying such constraints. Therefore, we propose a coalitional graph game based approach, which iteratively derives a transmission graph to reach every interested user. Simulations are conducted to compare the proposed approach with the optimal solution and two other reference schemes. The simulation results demonstrate the high performance of our approach in various scenarios with different network scales and social connections.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".