Risk-based Trust Evaluation Model for VANETs
Bibliographic record
Abstract
Vehicular ad hoc networks (VANETs) have drawn a lot of attention in recent years due to their potential in improving traffic safety applications. Evaluating trust between peers in such networks is an essential component that determines whether a received report from a neighboring vehicle should be accepted or refused. For this purpose, many VANET trust management models have been proposed, differing in their architecture, trust establishment process, and flexibility. However, risk estimation has not been taken into consideration in all of these models. In this paper, we propose a risk-based trust evaluation model that overcomes the information oversampling issue in VANETs. The proposed model provides a decision-making process for vehicles receiving conflicting reports regarding an event's occurrence according to the risk estimation for each required action of both reports. The risk is estimated according to the likelihood of taking an incorrect action and its associated impact. Finally, a decision is made corresponding to the action with the lowest risk. We show that a risk-based decision-making scheme may take different actions than a purely trust-based method. Simulation results show that the risk-based trust model outperforms a purely trust-based model.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".