Energy-Aware Incentivized Data Dissemination via Wireless D2D Communications With Weighted Social Communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Device-to-device (D2D) communications are featured by high energy efficiency and spectrum efficiency, which offers a promising technique for data dissemination over wireless networks. In this paper, we propose a novel solution that exploits D2D communications to enable efficient data dissemination over wireless networks. To distribute some messages to a target group of users, the base station first identifies the most influential users, called initial sources or seeds, and fulfills their requests. Then, these source devices forward their received messages to remaining users via D2D communications. To incentivize the sources in data forwarding, we propose a monetary auction-based mechanism and a moneyless matching-based mechanism, which can be activated depending on the practical application scenario. The monetary mechanism obtains the global optimal solution and achieves truthfulness, but it involves transfer of monetary rewards. In contrast, the moneyless mechanism enables a lightweight implementation, and it guarantees two-sided stability so that both sides of users in data forwarding are willing to accept the pairing result. To expedite data dissemination, we take advantage of social-awareness in a manner different from many existing approaches. We exploit weighted social relationships, which can be direct or indirect links between any two users in a connected community. To evaluate the performance of the proposed approaches, we develop novel schemes to generate and preprocess synthetic and real tracing datasets. Extensive simulation results with both synthetic and real tracing datasets demonstrate that our approaches achieve high utilities.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 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