Graph-Neural-Network-Based WiFi Indoor Localization System With Access Point Selection
Bibliographic record
Abstract
With the popularity of mobile devices and the increasing demand for indoor localization services, the localization of indoor mobile users is becoming more and more popular. However, many existing methods of building the radio map require collecting the received signal strength (RSS) of a large number of access points (APs), which causes high-hardware costs and large storage. Additionally, the instability of RSS in the actual environment will have a detrimental influence on indoor localization, and the large multistory buildings will also create new challenges. In this article, we propose a localization model with the combination of the AP selection network and the graph-based location mapping network. This model selects the optimal APs through the AP selection network and reduces the number of required APs. Then, the connection mode of the selected APs is used to construct a graph describing the location of reference points and users. Besides, the graph neural networks are used to extract graph-level representation, effectively capturing the misaligned features. Moreover, evaluated on the UJIIndoorLoc and UTSIndoorLoc data sets, the proposed method could not only reduce the number of required APs while ensuring localization performance but also outperform several state-of-the-art methods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".