Dependency-Aware Task Offloading in Cooperative UAV-HAPS-Assisted Vehicular Networks
Bibliographic record
Abstract
The advent of Intelligent Transportation Systems (ITS) stimulated the deployment of connected and autonomous vehicles supporting computation-intensive and delay-sensitive applications. This evolution has underscored the challenges of user device’s limited resources in vehicular networks. Addressing them requires innovative schemes, particularly in the context of integrated Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform Stations (HAPS) networks. In this paper, we propose a novel UAV/HAPS-enabled vehicular framework aiming to enhance task management. It is predicated on a collaborative offloading scheme that caters to the Quality-of-Service requirements of multi-vehicular tasks and aims to minimize the overall energy consumption within the dynamic ITS environment. Moreover, the proposed scheme is designed to handle the decomposition of tasks into interdependent sub-tasks. Given the NP-hardness of the offloading problem, we develop an approach based on the Genetic Algorithm. Through simulations, we demonstrate the superiority of our method compared to benchmarks, in terms of successful service rate and energy consumption. Our method constitutes a substantial advancement in handling ITS services through a cooperative UAV-HAPS framework.
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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 | 0.000 | 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".