Game Theoretical Incentive for USV Fleet-Assisted Data Sharing in Maritime Communication Networks
Bibliographic record
Abstract
With the rapid proliferations of maritime applications, the data demands of unmanned surface vehicles (USVs) keep ever-increasing. However, due to limitations of resources (e.g., energy, storage, bandwidth, etc.) and high costs on data sharing, USVs do not provide data proactively, which hinders the efficiency of data sharing. To tackle these problems, in this paper, we propose a game based USV fleet-assisted data sharing scheme to enable data exchange among USVs. Specially, we firstly propose a data publish/subscribe framework, where USVs are categorized into publishers and subscribers, and a USV fleet is motivated as a broker to relay data from publishers to subscribers. Then, the optimal waypoints for data publishing are recommended to the USV fleet to improve its probability of acquiring data. Furthermore, a Vickrey-Clarke-Groves (VCG) reverse auction game is utilized for data publishing, which ensures that the data publishers bid for USV fleets with own truthful costs, so as to avoid false bidding of data publishers. A double auction game is then employed for data subscription, which balances the benefits between the USV fleet and the data subscriber. An incentive-based data sharing algorithm is finally designed to obtain the optimal bidding strategies for all game parties including data publishers, USV fleets and data subscribers. Extensive simulation results demonstrate that the proposed scheme efficiently increases the utilities of all participants, as compared to conventional schemes.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".