An efficient indoor localization system based on Affinity Propagation and Support Vector Regression

Abstract

Indoor localization using fingerprinting techniques became more attracting to researchers in recent years because of their high accuracy. However, unpredictable Received Signal Strength (RSS) is one of the challenges. In our proposed system, it can be reduced by using strong Access Points (APs) selection method to select a subset of reliable APs and decrease the input of feature dimension. In addition, the computational cost due to a large fingerprint database was addressed by using Affinity Propagation clustering algorithm. The efficient indoor localization system relies on Support Vector Regression (SVR) due to its advantage of high generalization ability. The detailed results of real experiments indicate that the proposed system using SVR achieved a high accuracy compared to the conventional indoor algorithm.