Scalable Blockchain-based Architecture for Massive IoT Reconfiguration
Bibliographic record
Abstract
With billions of IoT devices expected in the next few years, their management is an important issue to be resolved and, while being praised in the past decades, the centralized approach of cloud computing may not be adequate at this massive scale. In this context, the introduction of blockchain technology with a distributed approach has raised a lot of hypes in solving this scalability problem. This paper proposes a blockchain-based architecture design for scalable reconfiguration of massive IoT devices. A REST API event-based publish/subscribe mechanism is used to decouple the IoT devices from the blockchain operations for reducing resource utilization. Moreover, smart contracts, reconfiguration workflows are developed to facilitate the blockchain-based update process. To evaluate the feasibility and performance of the proposed architecture, a proof-of-concept testbed has been developed. Experimental results illustrate that the proposed architecture is capable of providing a scalable solution for delivering on-demand configuration changes with a negligible effect on the resource utilization on IoT devices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 0.001 |
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".