Blockchain-Enabled Computing Offloading and Resource Allocation in Multi-UAVs MEC Network: A Stackelberg Game Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned Aerial Vehicle (UAV) is a promising technology that can serve as aerial base stations to assist the Internet of Things (IoT) network and solve various problems, such as expanding network coverage, improving network performance, transmitting energy to IoT devices, and performing IoT compute-intensive tasks. However, due to the communication between UAVs and the migration of computing tasks, privacy and security during the computing offloading process are challenging issues. To this end, we design an air-to-air multi-UAVs MEC network system based on multi-coalition game, and introduce blockchain technology to ensure privacy and security between UAVs, effectively ensuring the security and confidentiality of computing offloading between UAVs. In this paper, the joint optimization problem of UAV channel selection, UAV location deployment, block processor decision, block processor transmission power, and block processor generation frequency is studied. The goal is to minimize the weighted average sum of energy consumption and delay for MEC task computing and blockchain task processing. To handle this intractable issue, the original problem is decomposed into two subproblems and solved alternately with each other. In addition, the Joint Convex Optimization and Stackelberg Game Hierarchical (JCSH) algorithm is proposed, which solves the problem of blockchain-enabled computing offloading and resource allocation. The simulation results show that the JCSH algorithm has better performance and stronger robustness compared to other algorithms under different parameter settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it