Improving BLE Beacon Proximity Estimation Accuracy through Bayesian\n Filtering
Bibliographic record
Abstract
The interconnectedness of all things is continuously expanding which has\nallowed every individual to increase their level of interaction with their\nsurroundings. Internet of Things (IoT) devices are used in a plethora of\ncontext-aware application such as Proximity-Based Services (PBS), and\nLocation-Based Services (LBS). For these systems to perform, it is essential to\nhave reliable hardware and predict a user's position in the area with high\naccuracy in order to differentiate between individuals in a small area. A\nvariety of wireless solutions that utilize Received Signal Strength Indicators\n(RSSI) have been proposed to provide PBS and LBS for indoor environments,\nthough each solution presents its own drawbacks. In this work, Bluetooth Low\nEnergy (BLE) beacons are examined in terms of their accuracy in proximity\nestimation. Specifically, a mobile application is developed along with three\nBayesian filtering techniques to improve the BLE beacon proximity estimation\naccuracy. This includes a Kalman filter, a particle filter, and a\nNon-parametric Information (NI) filter. Since the RSSI is heavily influenced by\nthe environment, experiments were conducted to examine the performance of\nbeacons from three popular vendors in two different environments. The error is\ncompared in terms of Mean Absolute Error (MAE) and Root Mean Squared Error\n(RMSE). According to the experimental results, Bayesian filters can improve\nproximity estimation accuracy up to 30 % in comparison with traditional\nfiltering, when the beacon and the receiver are within 3 m.\n
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".