Online Distributed Optimization for Energy-Efficient Computation Offloading in Air-Ground Integrated Networks
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Bibliographic record
Abstract
Driven by ever-increasing vehicular intelligent computation-intensive and delay-sensitive services, this paper investigates the computing offloading in unmanned aerial vehicle (UAV)-assisted vehicular networks. Due to the limited onboard energy and computational resources of the mobile entities (i.e., the vehicles and the UAV), it is significant to explore the collaborative computation among the vehicles, the UAV, and the terrestrial computing servers for improving energy efficiency (EE) while trading off the service delay. Unlike existing work in the literature that is based on offline settings with a global view, an online distributed mechanism is proposed to cope with the spatial and temporal variations of the networks. Specifically, upon the arriving tasks and the real-time channel conditions, mobile entities adaptively decide about the task offloading and computational resources allocation in parallel. Moreover, the UAV also designs its trajectory with the residual battery capacity taken into account. Theoretical analysis shows that the developed approach can achieve the EE-delay tradeoff as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[ {O(1/V),O(V)} ]$</tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V$</tex-math></inline-formula> being a control parameter, and can strike a flexible balance between them by tuning <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V$</tex-math></inline-formula> . Numerical results verify the theoretical analysis and reveal that the performance gain can be obtained over conventional methods in the EE performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it