WiFi-based Indoor Positioning using Low-cost Microcontrollers and Signal Fingerprinting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of positioning or localization is important in many applications, including cellular devices, sensor networks, Internet-of-Things, vehicles and human beings. For some applications, Global Positioning System (GPS)-based algorithms provide adequate accuracy. In many cases, cellular networks are used to augment GPS and other signals to improve the accuracy. However, in indoor environments, GPS does not work or does not provide satisfactory accuracy. In this paper, we propose a low-cost, high-accuracy solution to the problem of indoor localization. In particular, we focus on buildings with existing Wifi infrastructure. We use low-cost, low-power, WiFi-enabled microcontroller units to build a system that models wireless signal fingerprint data using simple machine learning methods. We demonstrate through experiments carried out in a university building that our system provides high accuracy. We also study the change in accuracy with the increase in number of microcontroller units used.
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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 | 0.001 |
| 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 it