Robust priority aware multi-criterion offloading in digital twin UAVs networks
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) play a critical role in replenishing the energy of power-constrained Internet of Things (IoT) devices, particularly in public safety operations, thereby maintaining continuous system functionality. Integrating Mobile Edge Computing (MEC) into UAV platforms enables offloading computational tasks to aerial nodes, optimizing resource utilization. Efficient orchestration of communication, computation, caching, and energy resources is imperative to maximize the benefits of UAV-assisted MEC networks. Additionally, ensuring high situational awareness is essential for supporting priority-based latency-sensitive applications. Digital twin technology can be instrumental in minimizing latency by generating a real-time digital representation of the physical infrastructure, enabling enhanced system monitoring and optimization. Accordingly, we have formulated an optimization problem to maximize the number of IoT devices UAVs can support while adhering to pefined constraints. The formulated problem is a mixed integer non-linear programming model. Additionally, the dynamic management of tasks with varying priorities and computational demands introduces a significant resource allocation and scheduling challenge. Our proposed approach entails an efficient task offloading and priority-based scheduling strategy that prioritizes tasks, allocating computational resources to those with higher priority. The approach encompasses a multi-stage offloading strategy combining an interior-point method with a learning algorithm to address the inherent complexity and provide a viable solution. Simulation results validate the effectiveness of the proposed approach, outperforming conventional methods. Specifically, the Penalty Function Method Heuristic combined with the Interior Point Method achieves superior user connectivity compared to the Simple Relaxation Heuristic strategy.
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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 | 0.000 |
| 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".