Comparison Study between PoW and PoS Blockchains for Unmanned Aircraft System Traffic Management
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3757.vid This paper presents a novel consensus algorithm for Unmanned Aircraft System Traffic Management (UTM). Blockchain as a highly secured consensus protocol was used for airspace allocation. A smart contract in charge of airspace allocation was designed based on the Ethereum blockchain. Hence, the action of sharding could be done to reduce the computational complexity in the swarm flight zone. According to the sharded flight zone, a decentralized voting platform was developed using two main approaches, namely Proof of Work (PoW) and Proof of Stake (PoS). A swarm flight zone was arranged using a number of 1000 UAS-S4 at different locations and heading angles. The performance of the designed decentralized consensus system was evaluated in terms of error rate and validation time. Although the PoS approach was faster than the PoW, the comparison study showed that the PoW approach was better than the PoS in terms of error rate.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".