Development of Indoor Positioning System Using RSSI and Beacon Weight Approach in iBeacon Networks

Alsmadi, Laial Alsmadi

Development of Indoor Positioning System Using RSSI and Beacon Weight Approach in iBeacon Networks

Alsmadi, Laial Alsmadi

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Publication Type:: Thesis
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Alsmadi, Laial Alsmadi
dc.date.accessioned	2020-04-23T04:36:25Z
dc.date.available	2020-04-23T04:36:25Z
dc.date.issued	2019
dc.identifier.uri	http://hdl.handle.net/10453/140207
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Increasing the location accuracy of the Indoor Positioning System (IPS) is an important research area in localization. Utilizing mobile beacons in IPS environment has made localization more accurate and cost-effective. This research developed a Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) using Kalman filter. This approach takes both the distance and improved RSSI measurements between beacon nodes into consideration. Kalman filter is applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm. The developed approach was applied and validated in IPS experiments using Bluetooth Low Energy beacons. The results show that this FRBW approach has better positioning accuracy and minimum location error, and can be applied in IoT applications in smart city.	en_AU
dc.format	Thesis (MSc)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/140207/3/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.title	Development of Indoor Positioning System Using RSSI and Beacon Weight Approach in iBeacon Networks	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

Increasing the location accuracy of the Indoor Positioning System (IPS) is an important research area in localization. Utilizing mobile beacons in IPS environment has made localization more accurate and cost-effective. This research developed a Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) using Kalman filter. This approach takes both the distance and improved RSSI measurements between beacon nodes into consideration. Kalman filter is applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm. The developed approach was applied and validated in IPS experiments using Bluetooth Low Energy beacons. The results show that this FRBW approach has better positioning accuracy and minimum location error, and can be applied in IoT applications in smart city.

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http://hdl.handle.net/10453/140207