Learning-Based Collaboration for Secure Transmission Effectiveness Maximization in Low Altitude MEC Systems
Bibliographic record
Abstract
Low altitude mobile edge computing (MEC) offers promising prospects through unmanned aerial vehicle (UAV) services. However, the scarcity of resources, coupled with the growing personalized demands of devices, complicates the effective balance in UAV-MEC systems. Furthermore, the increasing threat of active aerial eavesdropping (AAE) poses severe risks of eavesdropping and attacking, significantly affecting the security of offloaded computation. To overcome these challenges, we propose a learning-based secure transmission scheme against AAE for UAV-MEC systems. Specifically, a customized secure transmission effectiveness is designed to effectively guide the optimization of limited resources and trajectory to meet individualized secure requirements. Then, multi-dimensional resources, including offloading decision, transmit power, computation frequency and UAV trajectory, are jointly optimized to maximize the secure transmission effectiveness with satisfying the individualized computing demands of devices. To address the multi-variables-coupling and closed-form-lacking problem, a deep reinforcement learning-based collaboration of multi-networks trajectory optimization and resource allocation (DRCTORA) scheme is proposed, where deep neural network ordering preserving (DNOP) is employed to generate offloading decisions, and double deep Q-network (DDQN) is connected in DNOP to obtain the solutions of the other variables. Simulation results show that the proposed DRCTORA enhances the secure transmission performance compared with benchmarks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".