Trust modeling for message relay control and local action decision making in VANETs
Bibliographic record
Abstract
ABSTRACT In this paper, we present a trust‐modeling framework for message propagation and evaluation in vehicular ad hoc networks. In the framework, peers share information regarding road condition or safety, and others provide opinions about whether the information can be trusted. More specifically, our trust‐based message propagation model collects and propagates peers' opinions in an efficient, secure, and scalable way by dynamically controlling information dissemination. The trust‐based message evaluation model allows peers to derive a local action decision about whether to follow the information by evaluating the information in a distributed and collaborative fashion while taking into account others' opinions. Experimental results demonstrate that our proposed trust‐modeling framework promotes network scalability and system effectiveness, which are the two essentially important factors for the popularization of vehicular ad hoc networks, in information propagation and evaluation under the pervasive presence of false information. In particular, we clarify how our relay control serves to decrease the number of inappropriate actions taken on the basis of malicious information and enables honest peers to produce a greater number of deliveries within the network. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".