IoTMoF: A Requirements-Driven Modelling Framework for IoT Systems
Bibliographic record
Abstract
The engineering of IoT systems brings about various challenges due to the inherent complexities associated with such heterogeneous systems. In this paper, we propose IoTMoF, a model-driven framework for requirements development, design and code generation of IoT systems. IoTMoF supports platform-independent modelling (PIM) with the ReqMIoT environment, which covers use case modelling and generation of IoT ARM compliant domain models. The use case model addresses exceptional behaviour by specifying exceptional situations along with handling functionality. The PIMs are mapped to platform-specific models, namely an IoT information model and a statechart model. The statechart defines both normal and exceptional behaviour of the system. These models form the basis for the subsequent generation of code for the IoT platform. A configuration model containing details of the IoT devices is used to generate wrapper code for deployment. Our work is demonstrated with the use of a smart lights system.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".