Calibration Cost Reduction of Indoor Localization Using Bluetooth Low Energy Beacon
Mansur As*1, Hiroshi Shimizu*2, Brahim Benaissa*3, Kaori Yoshida*2,, and Mario Köppen*4
*1Department of Computer Science, Faculty of Mathematic and Natural Science, Universitas Negeri Medan
Jl. Willem Iskandar, Pasar V, Medan, Sumatera Utara 20221, Indonesia
*2Department of Human Intelligent Systems, Graduate School of Life Science and System Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0196, Japan
*3Department of Mechanical Systems Engineering, Toyota Technological Institute
Design Engineering Lab, 2-12-1 Hisakata, Tempaku Ward, Nagoya, Aichi 468-8511, Japan
*4Department of Creative Informatics, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology
680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Indoor localization based on Bluetooth low energy (BLE) beacons has been rapidly developed, and many approaches have been developed to achieve higher estimation accuracy. In these methods, the received signal strength (RSS) is the input. However, the measurement of indoor environments is affected easily; the signal may be reflected and attenuated by obstacles such as the human body, walls, and furniture, which creates a challenge for methods based on signal mapping. In this study, BLE signal characteristics are investigated in an indoor localization setting. An experiment is performed using one BLE beacon and multiple receivers installed at different wall and ceiling positions. The raw RSS is observed, and the relationship between the BLE beacon signal strength characteristics against the human body effect as well as the receiver’s placement in the observation area are discussed. Signal mapping is performed, where the signal strength is measured from all receivers simultaneously. The position estimation accuracy is examined based on different data scenarios. The results show that the estimation position estimated by the BLE beacon based on extensive BLE beacon data does not affect the estimation accuracy.
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