Blockchain and Machine Learning in Internet of Vehicles: Applications, Challenges, and Opportunities
Bibliographic record
Abstract
The Internet of Vehicles (IoV) has emerged as a promising technology for transforming transportation systems by leveraging intelligent services and data-driven decision-making. Leveraging machine learning (ML) techniques, IoV data offers various benefits, including enhanced traffic management, improved road safety, and personalized user experiences. However, centralized ML methods face challenges in scalability and security, hampering their effectiveness in large-scale IoV deployments. This article presents a scalable and secure framework that incorporates distributed machine learning and blockchain technologies into the IoV ecosystem to overcome these limitations. The proposed framework enables the distribution of ML algorithms among participating vehicles, with each vehicle training a local model using its data. By executing a consensus algorithm, Roadside Units (RSUs) aggregate local models to provide more personalized and intelligent services in a scalable manner. Furthermore, the integration of blockchain ensures safety, transparency, and untampered features, thereby enhancing the overall security of the IoV system. This framework holds the potential to advance the efficiency, scalability, and security of IoV applications, paving the way for the widespread adoption of intelligent services in the transportation domain.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".