Digital Twin-Assisted Task Offloading with Chance Constrained Optimization in UAVs Networks
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) networks equipped with mobile edge computing (MEC) servers are increasingly deployed to deliver on-demand computing services in infrastructure-limited areas. However, the dynamic nature of UAV networks and their limited resources present significant challenges for task offloading and resource allocation. To address these challenges, we propose a framework that integrates the digital twin (DT) to optimize task offloading under uncertainty of energy consumption. The DT acts as a virtual replica of the UAV network, offering real-time predictions of local latency and offloading delays, enabling more accurate and adaptive decision making. We formulate a chance-constrained optimization problem to minimize task latency, ensuring that energy consumption exceeds a predefined threshold with only a limited probability. We propose a two-stage approach that combines an intelligent UAV placement algorithm with a modified iterative solver, referred to as the Renaldi algorithm, to solve the optimization problem efficiently and with low computational complexity. The simulation results show that the DT-assisted framework enhances user connectivity, improves resource utilization, and reduces computational complexity.
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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 | 0.001 |
| 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 | 0.001 | 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".