Analysis of indoor Wi-Fi localization using gaussian process regression and K-nearest neighbor algorithms

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Myo Myint Maw
Hnin Mya Nandar Myo Tint
Sarun Duangsuwan


Global positioning system (GPS) cannot well localize in indoor environment. Nowadays, indoor Wi-Fi localization is very attractive in positioning and localization research areas because of existing Wi-Fi infrastructure can be used to conduct, so there is no need extra hardware requirements and cost is not expensive. In indoor Wi-Fi localization, the received signal strength indicator (RSSI) fingerprinting played a key role in the access point performance services. This paper proposed the accuracy analysis of indoor Wi-Fi signal based on localization using machine learning approach. Four accept points (APs) were used to measure RSSI and the measured RSSI data were configured as RSSI fingerprint database which was composed of RSSI data of each AP and the position of receiver point. Four corridors which has 1m x 1m has were researched for indoor Wi-Fi localization. 3000 training RSSI and 234 testing RSSI data points were applied in indoor localization. The machine learning algorithms: Gaussian process regression (GPR) and K-nearest neighbor (KNN) approaches were proposed to analyze the accuracy of indoor localization. The results of Wi-Fi localization accuracy were shown using GPR and KNN in indoor environment. The accuracy and mean square error were discussed for indoor Wi-Fi based on GPR and KNN. In this proposed Wi-Fi indoor localization, the accuracy using GPR performed 75%. Moreover, the accuracy using KNN was 83% when K neighbor value was five. When K neighbor value was 10, the accuracy also outperformed 83%. Finally, according to the results, the analysis of corridors using KNN could provide more accuracy than GPR for testing environment in this study.

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