Empirical Study of a Room-Level Localization System Based on Bluetooth Low Energy Beacons

The ability to locate an object or a person at room-level inside a building or a house could have multiple applications. In this study, we adapt the fingerprint technique using Bluetooth Low Energy to locate the exact room of a person, seeking a simple and low-cost solution. The system is based on BLE beacons deployed at fixed positions and a person carrying a BLE scanner that generates fingerprints from the BLE beacons in coverage. We formulate it as a classification problem where each room is a class; the objective is to estimate the exact room, trying to maximize the area and number of rooms, but also trying to minimize the number of BLE beacons. The room estimation engine is based on a kNN (k-nearest neighbors) classifier. We evaluate the accuracy in two real scenarios and empirically measure the room estimation success related to the number of BLE beacons. As a proof-of-concept, a laptop and a Raspberry Pi are used as BLE scanners to test different hardware. We follow a measurement campaign for several days at different times to evaluate the stability and repeatability of the system. With just a few beacons an accuracy between 70 and 90% is achieved for house and university scenarios.