Blockchain-based Collision Avoidance Access Protocol for UAV Swarm
Bibliographic record
Abstract
The explosive growth of unmanned aerial vehicles (UAVs) pose more and more aerial and communication resources, making efficient spectrum access control a key bottleneck for UAV swarm applications. Conventionally, the absence of a centralized authority may cause lots of spectrum collisions among UAVs, which not only wastes the spectrum resources, but also causes long access delay. To address this challenge, we propose a blockchain-based access protocol that can effectively mitigate the access collisions among a distributed UAV swarm in high dynamic scenarios Specifically, we design a collision avoidance access mechanism by leveraging blockchain and hash access. We also analyze the performance of the proposed protocol and compare it with the slot multi-channel Aloha protocol. Simulation results demonstrate that the proposed protocol can effectively reduce the spectrum access collisions and is superior to slot multi-channel Aloha in access success rate, throughput, and average access delay.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".