Quantitative comparison of indoor positioning on different densities of WiFi arrays in a single environment
Bibliographic record
Abstract
Location Based Services (LBS), which are supported by ubiquitous location finding and positioning information, have become increasingly popular. These services are integrated with various wireless technologies (i.e. cellular, WiFi, and Bluetooth) on mobile devices that establish necessary location information. While each of these technologies contributes to the integration, development and success of LBSs, WiFi has been the most widely employed as an alternative to the Global Positioning System (GPS). Several commercial WiFi-based positioning systems (WPS) are available to the public to extend positioning coverage to places where GPS is unreliable or unavailable; however, these commercial WPSs often fail to deliver GPS-like positioning. The coarse positioning accuracy of commercial WPSs may be caused by unreliable or unsecured databases, which contain the essential WiFi-array information to produce local positioning. Knowing this, the quality of WiFi-based positioning services can be enhanced by improving the quality of a database with well-surveyed and accurate information. The Saskatchewan Enhanced positioning System (SaskEPS) is designed to reduce common errors in WPS. SaskEPS's positioning accuracy and consistency is supported by a thoroughly validated Access Point (AP) database. It has been tested in several buildings at the University of Saskatchewan and successfully provides GPS-like positioning accuracy. Our tests have also begun to elucidate the role of WiFi density in ensuring GPS-like positioning accuracy in indoor spaces. In this paper, we investigate the quantitative relationship between WiFi density and SaskEPS's overall positioning accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".