Indoor localization based on subarea division with fuzzy C-means

Junhuai Li, Jubo Tian, Rong Fei, Zhixiao Wang and Huaijun Wang

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 8, 1550147716661932

Abstract: One of the most significant researches in location-based services is the development of effective indoor localization. In this work, we propose a novel model of fingerprint localization, which divides location area into different subareas by fuzzy C-means and calculates location via relative distance fuzzy localization. In offline training stage, fuzzy C-means algorithm is used in localization model to divide localization area into different subareas and then to select the useful access points in subareas to reduce the dimensions of fingerprint. In online location stage, we use the nearest neighbor algorithm to select the subareas and to calculate the coordinate of the target point according to relative distance fuzzy localization algorithm, which converts traditional fingerprint of reference points into distance fingerprint and calculates the coordinate of the target point by fuzzy C-means algorithm. The noise and non-linear attenuation between the wireless signal and distance are taken into full consideration in relative distance fuzzy localization algorithm, which eliminates the random environmental noise. Experiments show that our proposed model is able to save the calculation time and improve the localization accuracy.

Keywords: Fingerprint; fuzzy C-means clustering; subarea dividing; access point selection; relative distance (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:8:p:1550147716661932

DOI: 10.1177/1550147716661932

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