Novel IoT Development Kit for Personalized Smart Ecosystems: Aliot
Bibliographic record
Abstract
The Internet of Things (IoT) is a growing field in computer science that needs more experts than ever before. In fact, there are currently 12.2 billion active connections of things, and it is predicted that this number will go up to 27 billion by 2025 [1]. Creating a complete IoT ecosystem is a time-consuming task and most of the time represents unnecessary trouble for a newcomer in this field. In this context, this paper proposes Aliot, an advanced development kit designed to help researchers and learners in their IoT projects. Aliot offers flexible tools available in two of the most popular programming languages in IoT, Python, and C++. It handles most of the required layers in a connected ecosystem while offering a lot of freedom to the end-user. It provides a reliable and secure connection for multiple connected devices with many unique features such as real-time monitoring and data management. Aliot was tested and used in many research projects, such as a smart city and a connected greenhouse. Preliminary results show that Aliot outperforms the traditional approach of developing an IoT ecosystem by reducing the amount of code written by more than 80% and 10 times faster to develop.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".