Joint Trajectory Planning, Application Placement, and Energy Renewal for UAV-Assisted MEC: A Triple-Learner-Based Approach
Bibliographic record
Abstract
In this article, an energy-efficient scheduling problem for multiple unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) is studied. In the considered model, UAVs act as mobile edge servers to provide computing services to end-users with task offloading requests. Unlike existing works, we allow UAVs to determine not only their trajectories but also the decisions of whether returning to the depot for replenishing energies and updating application placements (due to their limited batteries and storage capacities). With the aim of maximizing the long-term energy efficiency of all UAVs, i.e., the total amount of offloaded tasks computed by all UAVs over their total energy consumption, a joint optimization of UAVs’ trajectory planning, energy renewal, and application placement is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we reformulate such optimization problem as three coupled multiagent stochastic games. Since the prior environment information is unavailable to UAVs, we propose a novel triple-learner-based reinforcement learning (TLRL) approach, integrating a trajectory learner, an energy learner, and an application learner, for reaching equilibriums. Moreover, we analyze the convergence and the complexity of the proposed solution. Simulations are conducted to evaluate the performance of the proposed TLRL approach, and demonstrate its superiority over counterparts.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".