An Efficient Online Task Offloading Algorithm for Bilevel UAV-Enabled Mobile Edge Computing
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) enabled Mobile Edge Computing (MEC) has been an attractive paradigm for providing flexible and high-quality offloading services to ground users. In this paper, we study a hierarchical aerial MEC network architecture for improved quality of user experiences. Specifically, we study a bilevel UAV-enabled MEC network where a fixed-wing UAV (F-UAV) and multiple rotor UAVs (R-UAVs) are jointly deployed to provide continuous MEC services to ground users with dynamic demands. We formulate a long-term optimization problem for minimizing the utility of all users while considering the stability of task queue backlogs and energy consumption budgets at users and R-UAVs, where user utility measures the per-slot task processing performance at user side. We apply Lyapunov optimization technique to decompose the original problem into deterministic per-slot optimization subproblems. We derive the optimal offloading conditions at different types of edge nodes. We accordingly propose a Bilevel UAVs based Online Task processing and Resource allocation Algorithm (BOTRA) for determining the offloading and local processing profiles at users. Extensive simulation experiment results show the high performance of the proposed BOTRA algorithm compared with benchmark algorithms.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 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".