EDR: Efficient Decentralized Revocation Protocol for Vehicular Ad Hoc Networks
Bibliographic record
Abstract
It is well recognized that security is vital for the reliable operation of vehicular ad hoc networks (VANETs). One of the critical security issues is the revocation of misbehaving vehicles, which is essential for the prevention of malicious vehicles from jeopardizing the safety of other vehicles. In this paper, we propose an efficient decentralized revocation (EDR) protocol based on a novel pairing-based threshold scheme and a probabilistic key distribution technique. Because of the decentralized nature of the EDR protocol, it enables a group of legitimate vehicles to perform fast revocation of a nearby misbehaving vehicle. Consequently, the EDR protocol improves the safety levels in VANETs as it diminishes the revocation vulnerability window existing in conventional certificate revocation lists (CRLs). By conducting detailed performance evaluation, the EDR protocol is demonstrated to be reliable, efficient, and scalable.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".