Indoor Occupancy Prediction using an IoT Platform
Bibliographic record
Abstract
Current research in indoor sensor networks has pointed out an emerging interest in occupancy detection for Building Information Management (BIM) because buildings use 68% of Canadas energy in operation and contribute 17% of greenhouse gas (GHG) emissions. This research paper aims at developing a non-intrusive sensing method for predicting occupancy towards reducing building emission while also promoting a comfortable and productive working environment, while retaining the privacy of occupants. Towards this end, an IoT platform consisting of three main components: the edge computing environment, cloud based infrastructure, and network communication, together create a robust open source IoT architecture. The open source IoT architecture employs temperature, humidity, and pressure sensors for observing ambient environmental characteristics while combining PIR motion sensors, CO2, and sound detectors. An occupancy detection model is then developed by applying Support Vector Machine (SVM) to predict occupancy patterns from the incoming IoT sensor data. This platform is a low-cost and highly scalable both in terms of the variety of on board sensors and portability of the sensor nodes, which makes it well suited for multiple applications related to occupancy and environmental monitoring.
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 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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".