A Context-Aware Trust-Based Information Dissemination Framework for Vehicular Networks
Bibliographic record
Abstract
Reliable, secure, private, and fast communication in vehicular networks is extremely challenging due to the highly mobile nature of these networks. Contact time between vehicles is very limited and topology is constantly changing. Trusted communication in vehicular networks is of crucial importance because without trust, all efforts for minimizing the delay or maximizing the reliability could be voided. In this paper, we propose a trust-based framework for a safe and reliable information dissemination in vehicular networks. The proposed framework consists of two modules such that the first one applies three security checks to make sure the message is trusted. It assigns a trust value to each road segment and one to each neighborhood, instead of each car. Thus, it scales up easily and is completely distributed. Once a message is evaluated and considered to be trustworthy, our method then in the second module looks for a safe path through which the message is forwarded. Our frameworks are application-centric; in particular, it is capable of preserving traffic requirements specified by each application. Experimental results demonstrate that this framework outperforms other well-known routing protocols since it routes the messages via trusted vehicles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".