Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach
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Bibliographic record
Abstract
The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ETX$ </tex-math></inline-formula> ) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it