Dynamic WIFI-Based Indoor Positioning in 3D Virtual World
Bibliographic record
Abstract
Abstract. A web-based system based on the 3DTown project was proposed using Google Earth plug-in that brings information from indoor positioning devices and real-time sensors into an integrated 3D indoor and outdoor virtual world to visualize the dynamics of urban life within the 3D context of a city. We addressed limitation of the 3DTown project with particular emphasis on video surveillance camera used for indoor tracking purposes. The proposed solution was to utilize wireless local area network (WLAN) WiFi as a replacement technology for localizing objects of interest due to the wide spread availability and large coverage area of WiFi in indoor building spaces. Indoor positioning was performed using WiFi without modifying existing building infrastructure or introducing additional access points (AP)s. A hybrid probabilistic approach was used for indoor positioning based on previously recorded WiFi fingerprint database in the Petrie Science and Engineering building at York University. In addition, we have developed a 3D building modeling module that allows for efficient reconstruction of outdoor building models to be integrated with indoor building models; a sensor module for receiving, distributing, and visualizing real-time sensor data; and a web-based visualization module for users to explore the dynamic urban life in a virtual world. In order to solve the problems in the implementation of the proposed system, we introduce approaches for integration of indoor building models with indoor positioning data, as well as real-time sensor information and visualization on the web-based system. In this paper we report the preliminary results of our prototype system, demonstrating the system's capability for implementing a dynamic 3D indoor and outdoor virtual world that is composed of discrete modules connected through pre-determined communication protocols.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".