The Smart-Condo: Optimizing Sensor Placement for Indoor Localization
Bibliographic record
Abstract
The Smart-Condo is a hardware/software platform that aims to support and assist an individual in performing a variety of everyday tasks within his/her living space. The key to achieving this goal is being able to recognize the individual's general activities in real-time, without impeding these activities or compromising privacy. Since location and movement constitute meaningful evidence for many everyday tasks (e.g., presence in the bathroom correlates with personal hygiene activities), we are motivated to develop an efficient, accurate, and noninvasive occupant-localization method. To this end, we propose a methodology for planning the deployment of an array of privacy-respecting binary motion sensors. In particular, given the geometric constraints of the deployment space, we generate a model of indoor mobility patterns typical for a single occupant. We then use this model as the basis for a specific optimization problem: maximizing a measure of how well the frequently-visited areas of the living space are covered by a number of sensors, subject to a cardinality constraint on this number. We argue this optimization objective is a good surrogate for maximizing localization accuracy, and prove that it bears exploitable properties that make it receptive to a simple optimization routine. As a result, we obtain sensor configurations with localization accuracy superior to that achievable with the same number of sensors placed manually or randomly in the same environment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".