Scalable Creditable-Committee-Based Blockchain Consensus Protocol for Multihop Wireless Networks
Bibliographic record
Abstract
Scalable consensus protocol is essential for providing high-throughput and secure blockchain services in wireless networks. In this article, we propose a scalable credible-committee-based blockchain consensus (SCBC) protocol for resource-limited multihop wireless networks, which contains the following key designs: 1) credit-based committee selection algorithm, which improves the system security by selecting credible committee members; 2) scalable credible-committee-based consensus algorithm, which supports efficient consensuses using small-sized committee and threshold signatures; and 3) criticality-based localized broadcast algorithm, which is designed to suppress broadcast redundancy and further improves the consensus efficiency. Thorough security analyses show that SCBC satisfies both safety and liveness properties, and can resist more attacks than traditional Byzantine fault-tolerant consensus protocols. We deduce the message complexity of SCBC. Extensive simulation results demonstrate that our proposed protocol SCBC outperforms existing work in terms of throughput and consensus latency.
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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 | 0.001 | 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 | 0.002 | 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 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".