Impact of Model Uncertainty and Sensor Deployment Geometry on the Precision of BLE RSSI-Based Indoor Positioning

Abstract

Indoor positioning systems based on Bluetooth technology have gained significant attention with the widespread adoption of Bluetooth Low Energy (BLE). The rapid growth of BLE-enabled devices—now exceeding eight billion worldwide—has enabled the development of cost-effective, location-aware applications. The precision of BLE indoor positioning systems depends on several factors, among which the quality of received signal strength indicator (RSSI) measurements and the spatial deployment of sensors are most critical. Existing research has largely focused on improving RSSI accuracy through techniques such as multichannel measurements, outlier detection, Kalman filtering, and regression modeling. In this work, we examine two primary components that govern positioning precision: 1) the uncertainty in the RSSI-distance model parameters, including the path-loss exponent, reference power, and raw RSSI values; and 2) the geometry of sensor deployment, which directly affects estimation precision through the geometric dilution of precision (GDoP). We demonstrate that optimizing sensor placement using centroidal Voronoi tessellation (CVT) reduces GDoP and substantially improves positioning precision. Comparative experiments across two deployment scenarios, one based on CVT, confirm that CVT-based sensor configurations yield significantly higher precision in BLE RSSI-based indoor positioning.