CTT: A Three-Layer Tree Consensus Mechanism for Consortium Blockchains With Enhanced Security and Reduced Communication Cost
Bibliographic record
Abstract
Practical Byzantine Fault Tolerance-based consensus mechanisms in consortium blockchains face challenges in scalability and communication efficiency. While recent approaches like HotStuff and Kauri have attempted to address these issues through star and tree communication structures, they still encounter limitations in security, communication costs, and node workload distribution. This article presents CTT, a novel consensus mechanism with a three-layer tree communication structure for consortium blockchains. CTT incorporates three key innovations: 1) A fixed three-layer architecture that reduces communication complexity between any two nodes toO(1), compared toO(logn) in existing tree-based approaches; 2) specialized role distribution among nodes at different layers to optimize workload and enhance system security; 3) an improved Borda counting method for efficient consensus node selection based on multiple attributes including verification rate, propagation rate, and storage space. The mechanism features dual middle-node communication paths with bottom nodes, providing enhanced fault tolerance and security compared to existing approaches. Experimental results demonstrate CTT's effectiveness in improving scalability and security while reducing communication overhead in consortium blockchain systems. The findings have the potential to significantly advance the performance and applicability of consortium blockchains in critical areas such as finance, supply chain, and healthcare.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".