Blockchain for V2X: Applications and Architectures
Bibliographic record
Abstract
Modern vehicles rely on data from a vast array of sensors such as radar and GPS equipment that can be shared with surrounding vehicles and other interested parties. Vehicle-to-Everything (V2X) is the collection of systems that enable such communication. Although this data sharing has the potential to improve both the safety and efficiency of vehicles, ensuring that what is shared has not been altered, deleted, forged, leaked, or otherwise tampered with remains a challenging problem. Today, blockchain technology allows a system's participants to come to an agreement (consensus) on the state of the system and its data in a decentralized, trustless manner. This new technology may be capable of securing V2X data, as well as enabling other useful V2X services such as payments. However, the V2X ecosystem poses several unique challenges that complicate the application of blockchain technology, not least of which is the vast number of communications that any proposed blockchain network will need to support. This paper gives an overview of V2X and blockchain technology, explores potential applications of blockchain within the V2X domain and justifies its importance. It also reviews, analyzes, and discusses various blockchain architectures that could support V2X applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".