Machine learning-based indoor localization and occupancy estimation using 5G ultra-dense networks
Why this work is in the frame
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Bibliographic record
Abstract
Nowadays, mobile applications need the location of the running devices to operate properly. This has increased the interest in indoor localization. Furthermore, the ability to sense mobile devices in indoor environments opens the door for building occupancy-count estimation. Studies have shown that occupant's detection and building occupancy-count estimation can be utilized to improve the efficiency of building operation and management. This research introduces new models to study the performance of such indoor localization and building occupancy-count estimation using the available technological advances in 5G Ultra-Dense Networks (UDNs). We propose an algorithm to collect the Received Signal Strength Indicator (RSSI) from User Equipments (UEs) and use it to build a fingerprinting database. We then use Machine Learning (ML) to estimate the location of the UEs in buildings from their RSSI values. Detecting users in the building is treated as a binary-classification problem. We then use various ML algorithms to build models for indoor occupancy-count estimation. Finally, the localization of users is used to estimate occupancy in specific sections of the building. The simulation results show that UDNs can provide accurate indoor localization, occupancy-count estimation in a building and in parts within the building.
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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 | 0.001 | 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 | 0.001 | 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