Efficient multi-tier, multiple entry PBFT consensus algorithm for IoT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it