A Model-Based BLE Indoor Positioning System Using Particle Swarm Optimization
Bibliographic record
Abstract
Indoor positioning systems (IPSs) have emerged as a research topic in mobile computing, enabling the tracking and location of mobile devices in indoor environments. In model-based IPSs, the received signal strength indicator (RSSI) is used to estimate the distance between wireless signal receivers and transmitters using signal propagation models. However, the indoor environment presents challenges that make distance estimation using RSSI difficult. In this article, we propose a new IPS that combines particle swarm optimization (PSO) with signal propagation models to improve the accuracy of mobile device positioning. The PSO algorithm is used to optimize the position estimation process by generating different particles in the map, while the signal propagation model is used to model the attenuation and reflection of wireless signals in each particle. Our MIPS-PSO system does not require any prior training nor any knowledge of the best parameters of the signal propagation model. We evaluated the performance of our system using data collected in a real indoor environment with Bluetooth-low-energy (BLE) devices. Our results show that the MIPS-PSO achieves an average error of 2.57 m, an improvement of 40% when compared to a traditional trilateration, model-based IPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 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".