Smart Indoor Space Simulation Methodologies: A Review
Bibliographic record
Abstract
Research on smart indoor space applications, which focus on the occupants’ trace, requires a design process consisting of deploying several devices (sensors and actuators) and developing software components (services). The design process in the real-world is labour-intensive, costly and time-consuming, which implies the need for a simulation-based prototyping framework for quickly simulating smart indoor space applications. An increasing body of literature has sought to provide simulation methodologies for smart indoor space applications. However, a unified taxonomy that represents the simulation tools in terms of their essential components and their specifications is missing. In this survey paper, we systematically collect the related papers and derive a taxonomy for the specifications of the smart indoor space simulation tools. We use the taxonomy in order to cluster the papers. Our results indicate that our proposed taxonomy can divide papers into meaningful groups. We review each group in detail, summarize each paper, and provide insights into their specifications.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".