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Authors: Aigerim Mussina and Sanzhar Aubakirov

Affiliation: Department of Computer Science, al-Farabi Kazakh National University, Almaty and Kazakhstan

ISBN: 978-989-758-377-3

Keyword(s): Bluetooth Low Energy, Indoor Positioning, RSSI, Machine Learning, Support Vector Machine.

Abstract: The problem of real time location system is of current interest. Cities are growing up and buildings become more complex and large. In this paper we will describe the indoor positioning issue on the example of user tracking, while using the Bluetooth Low Energy technology and received signal strength indicator(RSSI). We experimented and compared our simple hand-crafted rules with the following machine learning algorithms: Naive Bayes and Support Vector Machine. The goal was to identify actual position of active label among three possible statuses and achieve maximum accuracy. Finally, we achieved accuracy of 0.95.

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Paper citation in several formats:
Mussina, A. and Aubakirov, S. (2019). Improving Indoor Positioning via Machine Learning.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 190-195. DOI: 10.5220/0007916601900195

@conference{data19,
author={Aigerim Mussina. and Sanzhar Aubakirov.},
title={Improving Indoor Positioning via Machine Learning},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={190-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007916601900195},
isbn={978-989-758-377-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Improving Indoor Positioning via Machine Learning
SN - 978-989-758-377-3
AU - Mussina, A.
AU - Aubakirov, S.
PY - 2019
SP - 190
EP - 195
DO - 10.5220/0007916601900195

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