RTEAM: Risk-Based Trust Evaluation Advanced Model for VANETs
Bibliographic record
Abstract
In Vehicular ad hoc networks (VANETs), vehicles share and exchange information regarding road safety and traffic conditions. Thus, trust is established among vehicles to ensure the integrality and reliability of the received reports. Ensuring the security of VANETs is the key to enhance road safety, and for this purpose, several trust establishing, evaluation, and management models have been proposed. When a vehicle receives conflicting reports about an event such as a car accident from its neighboring vehicles, the receiving vehicle must decide which report has to follow. Therefore, the vehicle takes advantage of the available data about the report’s sender. Then, the vehicle takes the right action. To this end, we propose a Risk-based Trust Evaluation Advanced Model (RTEAM) based on Multifaceted Trust and Hop-based trust to take action. The proposed model provides a decision-making process according to the risk estimation for each required action of both reports (i.e., reports that deny or confirm the event). 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. The experimental results show that the proposed model shows that the risk-based trust model outperforms a purely trust-based model in terms of undefined cases and true positive rates.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".