Offloading Delay Minimization in Air-Ground Integrated Vehicular Edge Computing Network With Energy Harvesting
Bibliographic record
Abstract
Compared with the traditional vehicular edge computing (VEC), air-ground integrated vehicular edge computing (AGI-VEC) network composed of unmanned aerial vehicles (UAVs) and VEC has significant advantages such as seamless coverage, short transmission distance, low delay, and high throughput, which can provide the high quality-of-service for Internet of vehicles (IoV). In this paper, we investigate a joint resource allocation and UAV trajectory design problem in AGIVEC network with energy harvesting (EH) to minimize the task offloading delay. Since the problem is a mixed integer programming problem, we first reformulate the problem as a Markov decision process (MDP), and then relax discrete variables into continuous variables, after that, a joint resource allocation and UAV trajectory design (JRATD) algorithm based on the multi-agent deep deterministic policy gradient (MADDPG) method is proposed. Simulation results demonstrate that the proposed JRATD algorithm outperforms other algorithms in minimizing the offloading delay.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".