NSshard: Low-Cross-Shard Sharding via Account Partitioning for Blockchain-Based IoT
Bibliographic record
Abstract
The Internet of Things (IoT) links the physical world to computing systems, and blockchain presents an opportunity to address the issues of weak interoperability and security flaws within IoT. However, blockchain faces the challenge of low throughput and scalability. Sharding is a promising solution, but it divides the blockchain into multiple committees, making the attack cost of malicious nodes lower. Sharding also leads to a large number of cross-committee transactions, which degrades the system’s performance. In this article, we propose the NSshard sharding framework that provides secure and low-cross-committee scaling. NSshard consists of network sharding and state sharding. We first propose a reputation score-based network sharding, which assigns each node a reputation score to reward its honest verification of transactions and penalizes its malicious behavior. This network sharding uses a random but balanced distribution of reputation scores, thereby decreasing the risk of collusion. We also propose a graph-based account partitioning scheme for state partitioning. To reduce the amount of cross-committee transactions, the scheme uses an undirected weighted graph to depict accounts and transactions. We design two algorithms based on edge splitting and overlapping community discovery, respectively. We also propose a dynamic sharding method to handle new transactions. We conduct extensive experiments to evaluate the efficiency of the proposed framework based on Ethereum transaction data. The experimental results show that our proposed framework can reduce the number of cross-committee transactions by 34.8% at 128 committees compared to the Metis algorithm.
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".