Building a Framework for Internet of Things and Cloud Computing
Bibliographic record
Abstract
Internet of Things (IoT) is the concept of connecting multiple objects together in an Internet-based architecture. Applications built around this concept are constantly growing in variety and quantity. Technologies in IoT have been evolving rapidly and the alternatives also have increased quickly. As a result, it becomes challenging to conduct system and software trade-off analysis or select suitable IoT technologies for applications. The paper emphasizes variability management (consisting of alternative technologies) and aims to provide a framework as a result, which would allow or facilitate users to create their own IoT applications. In order to achieve this goal, we have adopted the idea of software product line engineering (SPLE) and created a framework with a layered architecture consisting of a Cloud Layer, a Central Hub Layer, and an End Devices Layer. The layers are loosely coupled with well-defined interfaces allowing for variability to be added at each layer. We were successfully able to create a framework which allows users to build their own applications, only being limited by the devices supported by the framework.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".