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Developing an Improved Fingerprint Positioning Radio Map using the K-Means Clustering Algorithm
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Publication Year
2020-01-01
Journal
International Conference on Information Networking
Publisher
IEEE Computer Society
Citation
International Conference on Information Networking, Vol.2020-January, pp.761-765
Keyword
Fingerprintingindoor localizationK-Means ClusteringRadio Map
Mesh Keyword
Acquisition systemsEnvironmental factorsFingerprintingIndoor localizationRadio mapsRss variance problemsSuitable solutionsWi-Fi - Technology
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsInformation Systems
Abstract
Recently, with the development of Wi-Fi technology and the increase of mobile devices, location-based services that provide user location have drawn much attention. One of the most utilized methods for an indoor-based location acquisition system is the fingerprinting matching method, which estimates the user's location by analyzing the strength of the Wi-Fi signal. This system, however, suffers from the RSS variance problem in which the signal strength is unstable due to environmental factors. Therefore, it is crucial to collect stable sample records, which can be achieved by collecting signal samples over a sufficient period and averaging them. However, this is not the most suitable solution since signal strengths tend to be reliant on the device used and the time that it was measured. Eventually, sampled signals tend to form groups of clusters with respect to their obtained attributes. In this paper, we propose a more accurate radio map-generating algorithm by finding out the optimal number of clusters and applying the K-means clustering algorithm. This process generates a more precise radio map than the average sampling model.
ISSN
1976-7684
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36578
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85082126773&origin=inward
DOI
https://doi.org/10.1109/icoin48656.2020.9016627
Journal URL
http://www.icoin.org/
Type
Conference
Funding
This research was funded by National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2017R1D1A1B03035229)
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Lee, Chaewoo Image
Lee, Chaewoo이채우
Department of Electrical and Computer Engineering
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