Location-Based Services in Complex Indoor Environments via Modern Smartphone Multi-Sensor Integrated Navigation
Bibliographic record
Abstract
The problem of location-based services (LBS) in indoor environments, such as shopping malls, airports, and other large and complex buildings, has increasingly become more attractive.LBS are a type of service that utilizes the geographical location of a user or device to provide relevant information or functionality to that specific location or point of interest [Huang et al. (2018)].These services typically rely on technologies such as Global Navigation Satellite Systems (GNSS), which are usually inefficient in indoor environments, Wi-Fi, Bluetooth, or Wireless Sensor Networks to determine the user's position in real-time.Nowadays, modern smartphones are equipped with many useful sensors, some of which are particularly important for mapping, positioning, and navigation in indoor environments [Zhuang et al. (2016), Al-Balasmeh et al. (2024)].Recently, researchers have concentrated on enhancing the precision of positioning and navigation outputs through the integration of these sensors.The sensors used for integrated navigation are listed in the following, based on the type of measurement they provide.The first category is radio-based sensors that measure geometric quantities, such as distance or angle, either directly or indirectly.This includes GNSS, Wi-Fi, Bluetooth, including Bluetooth Low Energy (BLE), and, more recently, Ultra-Wideband (UWB).The second category is Inertial Measurement Units (IMUs), which measure physical quantities such as linear acceleration and angular velocity using Micro-Electro-Mechanical Systems (MEMS) accelerometers and gyroscopes, all within the smartphone's body frame.Finally, other sensors, such as the magnetometer (or magnetic compass), barometer, cameras, and pedometer, can also be used to determine location parameters through integrated navigation algorithms.For example, some useful Android and iOS applications collect this data simultaneously, allowing users to integrate it for LBS approaches.In this research, we introduce several of these applications based on the types of measurements, as shown in Table 1.App Android iOS Bluetooth Wi-Fi IMUs Magnetometer Barometer Pedometer Visual Sensor Logger Yes Yes Yes Yes Yes Yes Yes Yes Yes phyphox Yes Yes
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".