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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".