Early-stage Conflict Detection in HLF-based Delay-critical IoT Networks
Bibliographic record
Abstract
Hyperledger Fabric (HLF), a permissioned blockchain network recognized for high throughput and low transaction latency, has proven instrumental in augmenting security and privacy. However, with the surge in Internet of Things (IoT) devices, the volume of transactions relayed to the HLF blockchain has grown, leading to the problem of conflicting transactions. This problem occurs when two transactions attempt simultaneous read and write operations on the same ledger key. Given the time-sensitive nature of certain delay-critical IoT networks, these conflicts, resolved during the validation stage, can adversely affect key network performance metrics, including throughput and transaction latency. Current solutions, while addressing these conflicts, often fail to detect them at an early stage, resulting in performance degradation. To address this problem, we introduce a novel Early-stage Conflict Detection (ECD) mechanism. Designed specifically for HLF-based delay-critical IoT networks, the ECD mechanism employs a local cache, namely Sync.Map, to store and compare incoming transactions, enabling earlier conflict detection. Our results show that the ECD mechanism significantly improves valid throughput, transaction latency, and conflict detection time compared to existing approaches.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".