IntroductionAs the penetration rate of smart devices has recently increased, location-based service (LBS) has been applied to real life in various fields, and research on improving the accuracy of location recognition has been actively carried out [1][2][3][4]. Location awareness can be divided into outdoor and indoor areas. Users spend a significant amount of time indoors, and the need for such awareness is thus increasing, and many studies in this field are underway [5][6][7][8]. Although a typical outdoor location recognition technology uses the Global Positioning System (GPS), this system is difficult to use for indoor location recognition because it has a large position error and blocks signals on buildings and walls [9]. Therefore, methods using indoor WiFi, Bluetooth, RFID, and other approaches have been studied for indoor location recognition [10][11][12][13].
AbstractVarious 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.
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