Double-Layer Blockchain and MEC Deployment Enabled Secure and Efficient Entity Interaction Framework for the Industrial IoT
Bibliographic record
Abstract
The Industrial Internet of Things (IIoT), a core driver of Industrial 4.0, is considered as one of the most promising revolutionary technologies propelling the evolution of smart manufacturing towards Specialization, Reinforcement, Distinctiveness, and Innovation. The security and efficiency of smart manufacturing depend on the secure and efficient interaction of massive production data among entities. Yet, as a crucial measure of securing entity interactions, current authentication mechanisms overlook the single-point-of-failure issue and lightweight design. Moreover, interaction efficiency is rarely optimized and enhanced from the perspective of communication-supporting nodes. Paramountly, the assurance and optimization of entity interaction security and efficiency are strongly coupled, which is not considered in existing interaction frameworks. This paper designs a three-layer entity interaction framework based on mobile edge computing (MEC) and blockchain technology. Specifically, the double-layer blockchain and MEC-cluster assisted lightweight authentication (BCLA) mechanism is proposed under the three-layer framework to achieve lightweight entity authentication in a weakly centralized manner. To optimize the entity interaction efficiency from joint authentication and transmission, this paper further proposes an industrial edge server (IES) deployment optimization scheme and the proximity policy optimization based IES deployment (PAID) algorithm. The security features and efficiency of the three-layer framework are demonstrated by carrying out security analysis and performance evaluation, which is based on the Hyperledger Fabric platform.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".