Roadside unit‐based pseudonym authentication in vehicular ad hoc network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it