A Blockchain-Powered Traffic Management System for Unmanned Aerial Vehicles
Bibliographic record
Abstract
The increasing popularity and usage of unmanned aerial vehicles (UAVs) has brought about new challenges in airspace management. With the number of drones expected to grow even further in the coming years, there is an urgent need for an autonomous traffic management system (TMS) that can safely and effectively manage drone traffic in the airspace. It is critical that this TMS be built with principles of the Confidentiality, Integrity, and Availability (CIA) triad. In this paper, a traffic management system for UAVs is presented that takes advantage of a Hyperledger Fabric blockchain network. The TMS provides a decentralized and secure method to manage and deconflict drone flight paths, allowing for safe navigation in crowded airspaces. Through a series of simulated experiments, we demonstrated the system’s capabilities in handling path creation, multiple conflict resolutions, and large numbers of drones. Simulated tests showed that the proposed system was able to handle deconfliction of 1000 drones inside of a one square kilometer, and returned calculated paths for drones in 60 to 2000 ms with up to 100 deconflictions. The Hyperledger Fabric powered traffic management system showcased the potential to leverage permissioned blockchain technology in improving drone traffic management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".