Roadside unit‐based pseudonym authentication in vehicular ad hoc network
Bibliographic record
Abstract
Abstract Information sharing among vehicles is an essential component of an Intelligent Transportation System (ITS), but must take into consideration the security and privacy requirements of the network participants. Vehicular Ad Hoc Network (VANET) is a pervasive network, where vehicles communicate wirelessly with nearby vehicles, infrastructure nodes, and other connected devices to improve road safety and provide other useful services. Security of VANET can be improved by ensuring that only authorized vehicles can participate in the network. This research proposes a new approach that uses roadside units (RSUs) to authenticate safety messages and notify vehicles about any unauthorized messages/senders. We present a three‐tier architecture with a distributed blockchain to securely maintain the identity of all vehicles in the network. The use of this RSU‐based approach helps to reduce the computational overhead on the On‐board unit (OBU) of individual vehicles and does not require the transmission of lengthy certificates and certificate revocation lists with each message. Simulation results indicate that the proposed approach achieves improved performance compared to existing techniques in terms of both authentication delay and channel load.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".