Virtual Resources & Blockchain for Configuration Management in IoT
Bibliographic record
Abstract
Until now, most systems for Internet of Things (IoT) management, have been designed in a Cloud-centric manner, getting benefits from the unified platform that the Cloud offers. However, a Cloud-centric infrastructure mainly achieves static sensor and data streaming systems, which do not support the direct configuration management of IoT components. To address this issue, a virtualization of IoT components (Virtual Resources) is introduced at the edge of the IoT network. This research also introduces permission-based Blockchain protocols to handle the provisioning of Virtual Resources directly onto edge devices. The architecture presented by this research focuses on the use of Virtual Resources and Blockchain protocols as management tools to distribute configuration tasks towards the edge of the IoT network. Results from lab experiments demonstrate the successful deployment and communication performance (response time in milliseconds) of Virtual Resources on two edge platforms, Raspberry Pi and Edison board. This work also provides performance evaluations of two permission-based blockchain protocol approaches. The first blockchain approach is a Blockchain as a Service (BaaS) in the Cloud, Bluemix. The second blockchain approach is a private cluster hosted in a Fog network, Multichain
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".