Online Distributed Optimization for Energy-Efficient Computation Offloading in Air-Ground Integrated Networks
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$[ {O(1/V),O(V)} ]$with$V$being a control parameter, and can strike a flexible balance between them by tuning$V$. 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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".