Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper provides an overview of the Industrial Internet with the emphasis on the architecture, enabling technologies, applications, and existing challenges. The Industrial Internet is enabled by recent rising sensing, communication, cloud computing, and big data analytic technologies, and has been receiving much attention in the industrial section due to its potential for smarter and more efficient industrial productions. With the merge of intelligent devices, intelligent systems, and intelligent decisioning with the latest information technologies, the Industrial Internet will enhance the productivity, reduce cost and wastes through the entire industrial economy. This paper starts by investigating the brief history of the Industrial Internet. We then present the 5C architecture that is widely adopted to characterize the Industrial Internet systems. Then, we investigate the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management. This provides the foundations for those who are interested in understanding the essence and key enablers of the Industrial Internet. Moreover, we discuss the application domains that are gradually transformed by the Industrial Internet technologies, including energy, health care, manufacturing, public section, and transportation. Finally, we present the current technological challenges in developing Industrial Internet systems to illustrate open research questions that need to be addressed to fully realize the potential of future Industrial Internet systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it