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Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and Fingerprint Map

Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and... Abstract This paper presents analysis of object location accuracy of a mobile device on the basis of the iBeacon technology. The research starts with radio signal strength indicator analysis along the corridor in order to create a path loss model for iBeacon. Two cases are taken into account: line of sight and non-line of sight for model creation. For both cases two tests: Chi-square, Shapiro-Wilk have been performed. It has also been checked if the HCI (Host Controller Interface) is a source with a memory. Acquired data have been filtered with different type of filters, e.g. median, moving average and then compared. Next, the authors evaluated the indoor positioning trilateration algorithms with the use of created model for exemplary hall. The RSSI map (radiomap) was created and the logarithm propagation model was designed. The logarithmic model estimated distance with average error 1.09m for 1 – 9m and 1.75m for 1-20m and after trilateration, the positions with average error 2.45m was achieved. A statistical analysis for acquiring data led to the final conclusion which enhanced knowledge about positioning based on the popular iBeacon technology. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Electronics and Telecommunications de Gruyter

Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and Fingerprint Map

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Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
2300-1933
eISSN
2300-1933
DOI
10.1515/eletel-2016-0051
Publisher site
See Article on Publisher Site

Abstract

Abstract This paper presents analysis of object location accuracy of a mobile device on the basis of the iBeacon technology. The research starts with radio signal strength indicator analysis along the corridor in order to create a path loss model for iBeacon. Two cases are taken into account: line of sight and non-line of sight for model creation. For both cases two tests: Chi-square, Shapiro-Wilk have been performed. It has also been checked if the HCI (Host Controller Interface) is a source with a memory. Acquired data have been filtered with different type of filters, e.g. median, moving average and then compared. Next, the authors evaluated the indoor positioning trilateration algorithms with the use of created model for exemplary hall. The RSSI map (radiomap) was created and the logarithm propagation model was designed. The logarithmic model estimated distance with average error 1.09m for 1 – 9m and 1.75m for 1-20m and after trilateration, the positions with average error 2.45m was achieved. A statistical analysis for acquiring data led to the final conclusion which enhanced knowledge about positioning based on the popular iBeacon technology.

Journal

International Journal of Electronics and Telecommunicationsde Gruyter

Published: Dec 1, 2016

References