A Mining Strategy for Minimizing Waiting Time in Blockchains for Time‐Sensitive Applications
Bibliographic record
Abstract
Blockchain, as proposed in Bitcoin, focuses on securing financial transactions. However, in recent years, the use of blockchain has expanded to a wide range of networks and application domains. This includes time‐sensitive applications which need transactions to be processed fast enough to meet delay requirements. Reducing the transaction waiting time in the mining process is key to the successful adoption of blockchain in such applications. In this paper, we propose a mining strategy that is aimed at minimizing the average waiting time per transaction by ensuring a certain minimum required block size, based on the average transaction arrival rate, mining service rate, and maximum block size. We derive an expression for the average transaction waiting time of the proposed mining strategy and determine the optimal mining rule. Numerical results show that the average waiting time per transaction can be reduced by up to 15 % using the proposed mining strategy compared to the traditional strategy in which miners immediately start the next mining round using the transactions already waiting in the pool.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".