Energy-Aware Incentivized Data Dissemination via Wireless D2D Communications With Weighted Social Communities
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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How this classification was reachedexpand
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".