Multicriterion Digital Twin-Assisted Task Offloading With Chance-Constrained Optimization in UAV Networks
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) networks, which consist of UAVs equipped with mobile edge computing (MEC) servers, are becoming increasingly popular for providing on-demand computing services to IoT devices in areas lacking infrastructure. However, the limited resources of UAVs and the dynamic nature of the network environment present significant challenges for task offloading and resource allocation, underscoring the necessity of our research. This paper proposes a multicriteria optimization model for task offloading in UAV-MEC networks, taking into account the uncertainty in energy consumption. We formulate a chance-constrained optimization problem to minimize the weighted sum of latency and energy consumption while ensuring that the probability of exceeding energy constraints remains below a predefined threshold. We utilize a digital twin (DT) to estimate local computation latency and offloading delay to either the UAV-MEC server or a data center. We solve the formulated problem using branch and bound (BBA), simple relaxation (SR), integrality-gap minimization (IGM), and a gradient-based Renaldi heuristic under both intelligent and random placement. The simulation results indicate that our proposed approach is not just a theoretical concept but a practical solution that can be implemented. It outperforms existing methods in terms of number of connected users, overall utility, and computational complexity, demonstrating its real-world applicability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".