Efficient multi-tier, multiple entry PBFT consensus algorithm for IoT
Bibliographic record
Abstract
An implementation of a blockchain-based data storage and Internet of Things (IoT) system is described in this paper. A Practical Byzantine Fault Tolerance (PBFT)-like protocol is used to achieve consensus. The proposed approach consists of two layers, the lower layer with a number of clusters and the upper layer. The upper layer consists of virtual cluster composed of delegate nodes from lower clusters. Each cluster in the lower layer allows its member nodes to initiate simultaneous consensus rounds, implemented using a dedicated overlay network per node. Each overlay network is rooted in one node and connects it with every other node. This allows concurrent multiple entry PBFT consensus sessions in each lower layer cluster. In the upper layer, the virtual cluster members have to contend for linking their accepted blocks into the blockchain ledger. Performance analysis of the proposed approach is performed using a discrete-time Markov Chain (DTMC) and M/G/1 queuing-based analytical model. The efficiency of the proposed model is verified by testing over a wide range of parameter values.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".