Trust Management in Vehicular Ad-Hoc Networks: Extensive Survey
Bibliographic record
Abstract
Over the past few decades, Intelligent Transportation System (ITS) has become a vital and extensive element of daily human life and activity. Vehicular Ad hoc Networks (VANETs) have become the most promising components of ITS, which promises to enhance transport efficiency, passenger safety, and comfort by exchanging traffic and infotainment information to intelligent vehicles. Moreover, VANETs have emerged with new paradigms (e.g., Cloud, SDN (Software-Defined Networking), Fog computing, Blockchain, and AI (Artificial Intelligence) techniques) to provide strategic and secure communications to increase their reliability. Therefore, efficient and robust mechanisms, such as trust management, are essential requirements in VANETs. This survey provides an extensive overview of the VANET and trust management’s main concepts. After that, we briefly review existing surveys, followed by the significant challenges of security and trust in VANETs. Then, we identify, review, classify, summarize, and compare related approaches. Finally, we give some future research directions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".