A Lifecycle for Engineering IoT Neural Network-based Systems
Bibliographic record
Abstract
Internet of Things (IoT) applications have been deployed in several domains, including health care, smart cities, and agriculture. Because of the complex static and dynamic variability of the environment in which these applications are deployed, machine learning-based approaches have been used to support the design of IoT applications. In particular, an emergent approach involves using neural networks to enable IoT devices to learn to adapt their behavior based on the dynamics of the environment. Designing IoT systems is already challenging because of the autonomy and concurrency inherent in distributed physical systems. Moreover, neural networks systems have particular characteristics, such as dynamism, adaptability, and generalization, that make it necessary to adapt the traditional software development lifecycle to satisfy the requirements of these systems. In this paper, we describe our proposed approach to support the engineering of IoT neural network-based systems. Our approach considers a lifecycle supporting the integration of IoT system development tasks with particular ANN tasks, as model requirements and feature engineering. In addition, the paper includes the provision of the application of the approach to a case study and conclusive remarks.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".