Transaction Throughput Provisioning Technique for Blockchain-Based Industrial IoT Networks
Bibliographic record
Abstract
The proliferation of the IoT in connected society is rapidly expanding into vertical industry sectors due to the ever-increasing ties amongst businesses and economies. As the number of IoT nodes utilized in a network increases, decentralized network infrastructure, and security provisioning mechanisms, primarily enabled by blockchain-based technologies, become more beneficial. However, blockchain-based IoT networks experience transaction throughput degradation due to the platform's cryptographically-based security features, where negotiating with ledger maintainers for faster processing is a must. Existing transaction processing schemes are mainly geared towards digital currency applications. In overcoming these challenges, a novel feeless transaction processing algorithm is proposed for non-cryptocurrency blockchain-based IoT networks. The proposed algorithm enables ledger maintainers in achieving desired processing throughputs for select transactions found in a miner's transaction pool. A utility function is designed to select transactions from miners' transaction pools to form blocks that add a desired operational value for achieving pre-determined production outputs over blockchain-based networks. Furthermore, the proposed scheme will utilize an aging process to increase the likelihood of selecting transactions with larger miners' transaction pool residence times. The simulation and implementation results show that the proposed methodologies increase the processing throughput of desired transactions while preventing transaction processing starvation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".