Improve Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight Approach in iBeacon Network

Alsmadi, L; Kong, X; Sandrasegaran, K

Improve Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight Approach in iBeacon Network

Alsmadi, L Kong, X

Sandrasegaran, K

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 2019 19th International Symposium on Communications and Information Technologies, ISCIT 2019, 2019, pp. 42 - 46
Issue Date:: 2019-09-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 27 Sep 2021

Adobe PDF

Download Accepted Manuscript versionAdobe PDF (851.82 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Alsmadi, L	en_US
dc.contributor.author	Kong, X https://orcid.org/0000-0001-7855-858X	en_US
dc.contributor.author	Sandrasegaran, K https://orcid.org/0000-0003-1270-5756	en_US
dc.date.issued	2019-09-01	en_US
dc.identifier.citation	Proceedings - 2019 19th International Symposium on Communications and Information Technologies, ISCIT 2019, 2019, pp. 42 - 46	en_US
dc.identifier.isbn	9781728150093	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136215
dc.description.abstract	© 2019 IEEE. 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 paper proposes an Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) values using a Kalman filter. This algorithm takes both the distance and improved RSSI measurements between beacon nodes into consideration. Kalman filter applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm. The proposed algorithm was applied using eight 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_US
dc.relation.ispartof	Proceedings - 2019 19th International Symposium on Communications and Information Technologies, ISCIT 2019	en_US
dc.relation.isbasedon	10.1109/ISCIT.2019.8905189	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Improve Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight Approach in iBeacon Network	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-09-27T00:00:00+1000
pubs.publication-status	Published	en_US

Abstract:

© 2019 IEEE. 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 paper proposes an Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) values using a Kalman filter. This algorithm takes both the distance and improved RSSI measurements between beacon nodes into consideration. Kalman filter applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm. The proposed algorithm was applied using eight 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/136215