TCNS: An Efficient Trusted Cooperative Node Selection Model for Internet of Vehicles
Bibliographic record
Abstract
Internet of Vehicles (IoV) is an emerging technology to provide efficient and safe transportation by enabling vehicles to cooperate with each other or infrastructures through vehicle-to-everything (V2X) communications. However, cooperation among moving vehicles usually requires complex decision-making schemes to choose cooperative vehicles for security and quality guarantee, thus leading to a long time delay, which directly reduces collaboration efficiency among vehicles. In this regard, trust among vehicles can be utilized as a lightweight decision criterion to accelerate collaboration among moving vehicles. In this paper, we propose a trusted cooperative node selection model (TCNS) for IoV to select cooperative vehicles in an efficient way. In comparison with the traditional trust models, the proposed TCNS improves the accuracy of selecting appropriate cooperative peers by performing both direct and indirect trust evaluation in multi-dimension. Our proposed indirect trust evaluation model can achieve trust assessment without relying on infrastructure support in the vicinity. Furthermore, most of our trust evaluation steps are conducted in edge servers and roadside units (RSUs) as background processes, which saves time for trust establishment. The simulation results obtained show that our proposed model reduces the cooperation time effectively and increases the cooperation efficiency within vehicular networks.
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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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".