FTG+PM for the Model-Driven Development of Wireless Sensor Network based IoT Systems
Bibliographic record
Abstract
In recent years, various concepts, methodologies, and tools have emerged to tackle complexity of multi-paradigm systems using model-driven engineering (MDE) to improve usability, precision and automation of these systems. Multi-paradigm modelling (MPM) has been proposed to advocate the explicit modelling of all pertinent parts and aspects of these complex systems. Current modelling, analysis and simulation tools have limited capabilities to describe the engineering process that benefit from multi-paradigm approach. FTG+PM has been proposed as a basis for unifying key MDE practices, namely multi-paradigm modelling, meta-modelling, and model transformation. It enables the MDE lifecycle of these complex systems, including activities such as requirements development, domain-specific design, verification, simulation, analysis, calibration, deployment, code generation and execution, to be represented. In this exemplar paper, we apply the FTG+PM approach to the Wireless Sensor Network (WSN) based Internet of Things (IoT) domain and we describe the MDE process for developing applications for different platforms or operating systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".