UAV-Assisted Computation Offloading in Vehicular Networks
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) technology has recently attracted interest due to its rapid and flexible deployment. It became a key component of several applications such as aerial delivery and precision agriculture. Moreover, with enhanced payloads, e.g., storage and computing, UAVs can support critical services including road traffic monitoring, accident prediction, and connected-automated vehicles (CAVs). Particularly, computing-enabled UAVs permit CAVs’ task offloading. However, efficient offloading that accounts for the UAVs’ inherent characteristics remains under-investigated. In this context, we propose to study a UAV-assisted vehicular network, where a UAV flies according to a pre-defined come-and-go trajectory and communicates with nearby CAVs to offload their tasks. We target maximizing the ratio of successfully offloaded tasks by jointly optimizing the initial UAV launching point and traveling direction and strategically associating CAVs to the UAV for successful task offloading. Due to the formulated problem’s complexity, we propose two approaches to solve it, namely genetic algorithm (GA) based solution and an iterative exhaustive-linear programming (IE-LP) based one. Through experiments, we demonstrate the proposed algorithms’ superior performance in terms of task offloading success ratio compared to benchmarks, and in different conditions. These results can serve as guidelines for the development of more sophisticated UAV-enabled task offloading approaches in next-generation wireless networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".