Hybrid Resource Allocation Strategies for Efficient Task Offloading in Vehicular Edge Computing
Bibliographic record
Abstract
The deployment strategy of edge nodes in vehicular edge computing (VEC) is pivotal for optimizing information transmission and task offloading. To address the challenges posed by incomplete edge node coverage, dynamic traffic flow, and fluctuating service demands, this paper introduces a fast roadside unit (RSU) selection algorithm and a joint resource optimization method that integrates mobile RSUs (m-RSUs) and fixed RSUs (f-RSUs). Public transportation systems are leveraged as m-RSUs to enhance coverage in weak-signal or high-demand areas. Furthermore, the edge node deployment problem is formulated as a bi-objective optimization task, which is tackled using the Asynchronous Advantage Actor-Critic (A3C) algorithm. Simulation results demonstrate that the proposed approach significantly outperforms state-of-the-art methods in deployment efficiency and resource allocation, offering a scalable and adaptive solution for next-generation VEC systems.
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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 | 0.001 |
| 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 | 0.001 |
| 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".