Cooperative content delivery in UAV-RSU assisted vehicular networks
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) are gaining substantial attention owing to the great benefits offered to the vehicle users. In ITS paradigm, content data is normally obtained from road side units (RSUs). However, in some scenarios, terrestrial networks are partially/temporarily out-of-service. Unmanned Aerial Vehicle (UAV) or drone cells are expected to be one of the pillars of future networks to assist the vehicular networks in such scenarios. To this end, we propose a collaborative framework between UAVs and in-service RSUs to partial service vehicles. Our objective is to maximize the amount of downloaded contents to vehicles while considering the dynamic nature of the network. Motivated by the success of machine learning (ML) techniques particularly deep Reinforcement learning in solving complex problems, we formulate the scheduling and content management policy problem as a Markov Decision Process (MDP) where the system state space considers the vehicular network dynamics. Proximal Policy Optimization (PPO) is utilized to govern the content decisions in the vehicular network. The simulation-based results show that during the mission time, the proposed algorithm learns the vehicular environment and its dynamics to handle the complex action space.
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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 | 0.000 |
| 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".