LocFree: a WiFi RTT-based Device-Free Indoor Localization System

Abstract

Gaining knowledge of a person's location in the environment without needing a specialized device raises the need for device-free indoor localization systems that could be leveraged in many applications including IoT, security, etc. Wi-Fi is one of the most widely adopted technologies for indoor location determination tasks due to its ubiquity. Existing device-free indoor localization systems rely on the utilization of the Received Signal Strength Indicator (RSSI) and the Wi-Fi Channel State Information (CSI). However, RSSI is highly sensitive to environmental noise such as multi-path interference and fading which causes degradation in the system's performance. In addition, CSI suffers a lack of standardization which necessitates the requirement for special hardware or software. In this work, we present LocFree, a deep-learning-based device-free indoor localization system that handles the challenges of RSSI and CSI by leveraging the Time of Flight (ToF) information obtained using the IEEE 802.11mc Fine Time Measurement (FTM) protocol. The FTM protocol measures the Round Trip Time (RTT) between two Wi-Fi devices which is influenced by the human-body blockage. Consequently, LocFree trains a deep classification model using the RTT data indicating the person's existence in the area. Finally, LocFree employs a smoothing stage that enables location determination with fine-grained accuracy. The evaluation of LocFree in a realistic environment demonstrates its efficacy, achieving a median localization accuracy of 1.56m.