Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts

Indoor positioning has garnered significant interest over the last decade due to rapidly growing demand for location-based services. As a result, multitude of techniques been proposed localize objects and devices in indoor environments. Wireless fingerprinting, which leverages machine learning, emerged as one most popular approaches its low implementation cost. The prevailing fingerprinting-based mainly utilizes wireless fidelity (Wi-Fi) Bluetooth energy (BLE) signals. However, RSS Wi-Fi BLE signals are very sensitive layout environment. Thus, any change could potentially lead severe degradation terms localization performance. To foster development new methods, several open-source location fingerprinting datasets have made available research community. Unfortunately, none these public provides received signal strength (RSS) measurements environments with different layouts. fill this gap, paper presents hybrid dataset multi-floor layouts facilitate future systems that can provide adaptive performance dynamic Additionally, effects on fingerprint also investigated.