Robust Multicriterion Offloading in Digital-Twin-Assisted UAV Networks
Bibliographic record
Abstract
Unmanned-aerial-vehicles (UAVs) have been gaining much attention in the next-generation wireless networks due to their ability to enhance coverage and provide advanced services, particularly for first responders. UAVs equipped with mobile-edge computing (MEC) capabilities can migrate computational resources to airborne platforms. However, it is crucial to manage resources efficiently to optimize overall network performance. Moreover, in public safety scenarios, UAVs can help charge low-power Internet of Things (IoT) devices to sustain system operations. A holistic approach to managing communication, computation, caching, and energy resources is necessary to leverage UAV-assisted MEC networks fully. We formulated an optimization problem to minimize latency and reduce resource costs associated with communication, computation, caching, and energy harvesting while maximizing the number of IoT devices served by UAVs. Therefore, we integrated digital twin technology to analyze the latency. The optimization problem is challenging as it involves a mixed-integer nonlinear programming problem. To address this complexity, we propose a multistage offloading algorithm named the penalty function method heuristic algorithm that combines a learning algorithm with an interior-point method, ultimately delivering a practical solution. Our simulation results validate the performance of the proposed algorithm, which yields superior results compared to the simple relaxation heuristic algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".