Performance Optimization and Improvement of ISAC-Enabled Industrial IoT Based on Intelligent Sharding Blockchain
Bibliographic record
Abstract
The security and reliability risks of industrial sensors have constrained the integration of the integrated sensing and communications (ISAC) and the Industrial Internet of Things (IIoT). Although blockchain can protect the security and reliability through hash verification mechanisms, there are numerous challenges in the existing systems, such as the scalability bottleneck, high computational power consumption of consensus protocols and limited computational resources of industrial sensors. To address these problems, an intelligent sharding blockchain framework is proposed, in which the reputation mechanismbased intelligent sharding and the adaptive consensus protocol switching are utilized to enhance the decentralization, security and scalability of blockchain. Considering the higher requirements of computational power of the sharding blockchain, a cloud-edge-end collaborative computing framework is introduced, in which the parallel computational offloading and the Terahertz communication technology are utilized to enhance the cooperation between the cloud and edge networks. Furthermore, due to the dynamic characteristic of industrial sensors and industrial data, we formulate the optimization problem as a Markov decision process (MDP), which is solved via the Proximal Policy Optimization (PPO) algorithm. Simulation results show that our proposed scheme can minimize total latency and maximize transaction throughput while guaranteeing the safety as well as decentralization of the IIoT systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".