Cooperative Resource Scheduling for Environment Sensing in Satellite–Terrestrial Vehicular Networks
Bibliographic record
Abstract
In this article, we investigate infrastructure-assisted environment sensing in satellite-terrestrial vehicular networks (STVN) for connected autonomous vehicles (CAVs), where satellites and roadside units (RSUs) cooperate to provide CAVs with fresh sensing data. To support satellite- and RSU-assisted environment sensing for CAVs, we formulate a long-term resource scheduling problem in STVN to satisfy sensing data freshness requirements with efficient resource usage. To deal with the challenges posed by the dynamic network environment as well as stringent data freshness requirements, we propose a cooperative satellite-terrestrial resource scheduling (CSTRS) scheme. CSTRS is a model-data co-driven approach that can jointly optimize the sensing interval and resource allocation in STVN. Specifically, benefiting from the multicast feature of the low Earth orbit satellite, coalition game, and particle swarm optimization-based algorithms are designed to partition CAVs into groups and optimize sensing intervals in large timescales. Then, a reinforcement learning-based algorithm is developed to make real-time computing and communication resource allocation decisions based on the CAV partition. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and reliability performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".