Optimal Offloading of Computing-intensive Tasks for Edge-aided Maritime UAV Systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers the autonomous detecting and tracking task of the unmanned aerial vehicle (UAV) in the maritime environment. In the maritime UAV tracking system, due to the large size of the image computing-task and the shortage of UAV batteries and computational capability, the UAV needs to offload the computing-intensive task to the edge computing server (ECS) to reduce energy consumption and task latency. However, the task latency is still too long for the UAV tracking algorithm due to the large image size. We research the impact of image resolution on the computing task size and detection accuracy, and formulate an edge-aided UAV system with dynamic image resolution. With the constraint on task latency, we jointly optimize the image resolution, offloading rate, transmission power and local central processing unit (CPU) frequency to minimize energy consumption. Although the proposed problem is non-convex, we transform it into a convex optimization problem through decoupling and problem decomposition, and obtain an optimal offloading strategy. The numerical results show the energy efficiency of the proposed strategy by comparing it with the local first offloading strategy and the edge first offloading strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it