An Edge Intelligent Blockchain-Based Reputation System for IIoT Data Ecosystem
Bibliographic record
Abstract
Industrial Internet of Things (IIoT) devices generate and collect massive amounts of industrial data. Monetizing the flood of data generated by the IIoT devices has enabled the creation of the IIoT data ecosystem, where individuals and businesses may trade data. With the rapid expansion of the online data trading industry, the necessity for an edge intelligent reputation system is becoming increasingly important as more individuals and services connect online. In recent years, researchers have proposed blockchain-based reputation systems as a means of offering anonymity, security, transparency, and mutual trust for both providers and customers in Industry 4.0. Unfortunately, they focus on the decentralized reputation system with a single certificate authority, which creates the concern of a single point of failure (SPOF). Moreover, researchers paid little attention to the performance measures of these blockchain-based reputation systems to demonstrate their usability in a real IIoT data ecosystem. This article proposes a robust edge intelligent blockchain-based reputation system capable of avoiding failures by enhancing the Raft consensus mechanism. We provide extensive security analysis and simulation experiments to demonstrate the performance of the blockchain-based reputation system for the IIoT data ecosystem using different metrics, such as transaction throughput, latency, and resource consumption.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".