Potential risks of WiFi-based indoor positioning and progress on improving localization functionality
Bibliographic record
Abstract
Much effort has been expended to develop and improve indoor positioning. Many wireless sensor technologies have been used for indoor positioning systems; however WiFi has been the most widely employed sensor system as an alternative to Global Positioning System (GPS). Many commercial indoor positioning services such as those developed for and available on Apple and Android systems are hardly satisfying users' demand, primarily because of their inaccurate positioning. The Saskatchewan Enhanced Positioning System (SaskEPS) has been developed to provide reliable indoor positioning as a complement to GPS. SaskEPS successfully produces very reliable 2.5-Dimensional positioning (X-Y and floor) information at randomly selected fixed locations across an extensive indoor environment at the University of Saskatchewan. SaskEPS produces GPS-like positioning accuracy (sub 10 metre error) during testing; however there are several additional limitations that reduce the ability of non-GPS systems to provide accurate and reliable positioning indoors as compared to GPS. SaskEPS and other trilateration-based WiFi-based Positioning Systems can improve their positioning abilities with techniques commonly used in GPS-based positioning systems; therefore, SaskEPS has integrated a map-matching technique (Post-positioning correction) with its trilateration-based algorithm (Pre-positioning determination). In this paper we explore some limitations for WiFi-based indoor positioning with an explicit examination of SaskEPS in a complex multi-building environment. As well, some add-on localization functionalities are tested for reducing positioning errors and increasing reliability of SaskEPS.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".