Collaborative Offloading and 3D Trajectory Design in Multi-UAV-assisted MEC Networks for Emergency Rescue
Bibliographic record
Abstract
Unmanned Aerial Vehicle (UAV) offers flexible Mobile Edge Computing (MEC) services for mobile users (MUs). However, existing works generally assume that MUs are static and design UAVs' 2D flight path towards MUs. This paper presents a multi-UAV-assisted MEC networks for emergency rescue application, where MUs randomly move towards operation site and UAVs automatically track them to process their computation tasks. Edge collaboration between multiple UAVs is also considered to enhance resource utilization. Then, we formulate a joint computation offloading, UAV 3D trajectory design and resource allocation problem, with the goal of maximizing system utility. And a DRL-based UFCOR algorithm is proposed to solve the problem. Simulation results demonstrate that UFCOR enables UAVs to effectively track MUs and significantly improves system utility.
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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 | 0.002 |
| 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 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".