DND: Driver Node Detection for Control Message Diffusion in Smart Transportations
Bibliographic record
Abstract
Along with the development of IoT and mobile edge computing in recent years, smart transportation holds great potential to improve road safety and efficiency. The network that carries smart transportation service is highly dynamic. Controllability has long been recognized as one of the fundamental properties of such temporal networks, which can provide valuable insights for the construction of new infrastructures, and thus is in urgent need to be explored. In this article, under the smart transportation scenario, we first disclose the controllability problem in Internet of Vehicles (IoV), and then design DND (Driver Node Detection) algorithm based on Kalman's controllability rank condition to analyze the controllability and control message diffusion in such a dynamic temporal network. Moreover, we use the control message diffusion efficiency as a metric to assist in selecting suitable driver nodes. At last, we conduct a series of experiments to analyze the controllability of the IoV network, and the results show the effects of vehicle density, speed, coverage radius on network controllability, and the efficiency of the control message diffusion algorithm and its feedback effect on driver nodes selection. These insights are critical for varieties of applications in the future smart transportation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".