Enhancing Indoor Localization with Machine Learning and WiFi Signals from Mobile Devices

Indoor Positioning Systems (IPS) are crucial technologies that provide accurate location information in enclosed environments such as shopping malls, hospitals, and airports, where GPS signals are either inaccessible or significantly degraded. However, traditional indoor positioning methods like RFID, Ultrasonic, Ultra-Wideband, … often face challenges such as high costs, complex infrastructure requirements, and inconsistent accuracy. A new approach involves using Wi-Fi Probe Frames message from personal devices like smartphones, smart watch, laptop, …. When a Wi-Fi-enabled device searches for a network to connect to, it emits probe frames containing information such as MAC address and Received Signal Strength Indicator (RSSI). This approach offers several advantages: low deployment costs, no user interaction required, and the ability to estimate the location of the signal-emitting device with reasonable accuracy. Experimental results have shown that the maximum error is only 0.28 meter, accuracy up to 0.25m, and detection rate 93% in room. The proposed method can also achieve moderate computational complexity and moderate real-time performance compared to other methods.