INBS: An Improved Naive Bayes Simple learning approach for accurate indoor localization

Indoor localization based on WiFi signal strength fingerprinting techniques have been attracting many research efforts in past decades. Many localization algorithms have been proposed in order to achieve higher localization accuracy. In this paper, we investigate Bayes learning algorithms and some c...

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Bibliographic Details
Main Authors: Wenzhe Zhang, Lei Wang, Zhenquan Qin, Xueshu Zheng, Liang Sun, Naigao Jin, Lei Shu
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Buildings
IEEE 802.11 Standards
Laboratories
Machine learning algorithms
Mathematical model
Training data
Online Access:Request full text
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Summary:Indoor localization based on WiFi signal strength fingerprinting techniques have been attracting many research efforts in past decades. Many localization algorithms have been proposed in order to achieve higher localization accuracy. In this paper, we investigate Bayes learning algorithms and some common-used machine learning algorithms. We identify a general problem of Zero Probability (ZP) which may cause significant decrease of accuracy. In order to solve this problem, we propose an Improved Naive Bayes Simple learning algorithm, namely INBS, based on our data set characteristic. INBS is applicable even though Zero Probability problem occurs. We design experiments based on off-the-shelf WiFi devices, mobile phones and well-known machine learning tool Weka. Our experiments are conducted on a floor covering 560m 2 in a campus building and a laboratory covering 78m 2 . Experiment results show that INBS outperforms traditional Naive Bayes and k-Nearest Neighbors (k-NN) algorithms and two common-used machine learning algorithms in terms of accuracy.
ISSN:1550-3607
1938-1883
DOI:10.1109/ICC.2014.6883310