An Improved Cost-Effective Indoor Air Quality Prediction through Internet of Things Edge Network and Hybrid Model
Bibliographic record
Abstract
To enhance indoor air quality monitoring and decision-making, characterized by a four-layer Internet of Things (IoT) architecture involving data acquisition, processing, storage, and application we developed ICAIEH model. A Raspberry Pi with an Enviro+ board and sensors to track air quality metrics like particulate matter and carbon dioxide in real-time, securing data transmission through HTTPS and storing it in a MySQL database with local backups. The system includes revised diagnostic formulas that incorporate the concentration and duration of air pollutants, provides relative comparisons of Indoor Air Quality (IAQ), and applies mathematical functions for pollutant spike detection. It utilizes Particle Swarm Optimization (PSO) to improve the accuracy and efficiency of assessments, with reportable parameters indicating a productive heatmap process, multiple fitness analysis iterations, and an adequate execution time. The following parameters are used to calculate the ICAIEH model are heatmap process of 0.97, fitness of 200 iterations of .031, fitness of 48 iterations process of 0.30, energy consumption of 0.28, and execution time of 41.8.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".