RTE: Rapid and Reliable Trust Evaluation for Collaborator Selection and Time-Sensitive Task Handling in Internet of Vehicles
Bibliographic record
Abstract
By enabling connectivity and collaboration among moving vehicles, Internet of Vehicles (IoV) is expected to bring dramatically improved road safety and traffic efficiency. With limited onboard resources and real-time operational constraints, achieving these goals through handling time-sensitive IoV services and tasks inevitably relies on rapid and reliable collaboration among moving vehicles. Due to safety-related considerations, such collaboration always requires complex evaluation of potential collaborative vehicles, resulting in increased latency in time-sensitive IoV task handling. To achieve rapid and reliable IoV collaboration, a comprehensive concept of trust among neighboring vehicles is first conceptualized in this article to maximize Quality of Experience (QoE) by expediting the IoV collaborator selection as well as overall task handling. Specifically, we propose a new concept of indirect trust and the related Rapid and reliable Trust Evaluation (RTE) mechanism by enabling trust transfer from reliable third parties to reduce the trust evaluation latency of potential collaborative peers. Furthermore, capability trust and direct experiential trust are introduced as two additional evaluation factors in RTE to assess the capability and reliability of collaborators and to reduce task computation time. Finally, the different factors of the proposed trust, i.e., indirect trust, direct experiential trust, and capability trust, are integrated and adaptively utilized at different stages of IoV collaboration by a proposed adaptive trust factor aggregation scheme. Simulation results demonstrate that the proposed RTE mechanism achieves higher QoE with reduced task completion latency by swiftly selecting the optimal IoV collaborator compared to existing trust evaluation mechanisms.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".