Design and implementation of an IoT-Based air treatment Single-Room Ventilation System using ESP-NOW
Bibliographic record
Abstract
Growing concerns about both indoor and outdoor air pollution have highlighted the vital importance of Indoor Air Quality (IAQ), an issue amplified by recent lifestyle changes, including those arising from the COVID-19 pandemic. Although traditional ventilation systems still widely used, they present several limitations including their maintenance and cost as well as their inability to fully eliminate pollutants.This paper presents the design approach of an IoT-based Single Room Ventilation System. This system is dedicated to operating autonomously or as part of a networked system alongside other ventilation units within the same building.To ensure reliable communication between units, the ESP-NOW communication protocol was used. Moreover, a mobile application was developed and implemented using Dart and Flutter software enables users to monitor and control the system remotely. A database and IoT-based platform are used to collect and log real time parameters. This proposed solution aims to enhance IAQ management via user-friendly interface, accessible through both Android and iOS devices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".