Low-Code Internet of Things Application Development for Edge Analytics
Bibliographic record
Abstract
Abstract Internet of Things (IoT) applications combined with edge analytics are increasingly developed and deployed across a wide range of industries by engineers who are non-expert software developers. In order to enable them to build such IoT applications, we apply low-code technologies in this case study based on Model Driven Development. We use two different frameworks: DIME for the application design and implementation of IoT and edge aspects as well as analytics in R, and Pyrus for data analytics in Python, demonstrating how such engineers can build innovative IoT applications without having the full coding expertise. With this approach, we develop an application that connects a range of heterogeneous technologies: sensors through the EdgeX middleware platform with data analytics and web based configuration applications. The connection to data analytics pipelines can provide various kinds of information to the application users. Our innovative development approach has the potential to simplify the development and deployment of such applications in industry.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".