Random forest and WiFi fingerprint-based indoor location recognition system using smart watch

Various technologies such as WiFi, Bluetooth, and RFID are being used to provide indoor location-based services (LBS). In particular, a WiFi base using a WiFi AP already installed in an indoor space is widely applied, and the importance of indoor location recognition using deep running has emerged. In this study, we propose a WiFi-based indoor location recognition system using a smart watch, which is extended from an existing smartphone. Unlike the existing system, we use both the Received Signal Strength Indication (RSSI) and Basic Service Set Identifier (BSSID) to solve the problem of position recognition owing to the similar signal strength. By performing two times of filtering, we want to improve the execution time and accuracy through the learning of random forest based location awareness. In an unopened indoor space with five or more WiFi APs installed. Experiments were conducted by comparing the results according to the number of data for supposed system and a system based on existing WiFi fingerprint based random forest. The proposed system was confirmed to exhibit high performance in terms of execution time and accuracy. It has significance in that the system shows a consistent performance regardless of the number of data for location information.