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http://dx.doi.org/10.6109/jkiice.2022.26.1.70

Indoor positioning method using WiFi signal based on XGboost  

Hwang, Chi-Gon (Dept. of Computer Engineering, IIT, Kwangwoon University)
Yoon, Chang-Pyo (Dept. Of Computer & Mobile Convergence, GyeongGi University of Science and Technology)
Kim, Dae-Jin (Institute for Image & Cultural Contents, Dongguk University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.1, 2022 , pp. 70-75 More about this Journal
Abstract
Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.
Keywords
Indoor positioning; RSSI(Receiver signal strength indicator); Bagging; Boosting; XGboost;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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