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Visible light‐based indoor localization using k‐means clustering and linear regression
Author(s) -
Saadi Muhammad,
Saeed Zeeshan,
Ahmad Touqeer,
Saleem M. Kamran,
Wuttisittikulkij Lunchakorn
Publication year - 2019
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3480
Subject(s) - rss , cluster analysis , centroid , computer science , linear regression , regression , cluster (spacecraft) , signal (programming language) , artificial intelligence , pattern recognition (psychology) , light intensity , data mining , statistics , mathematics , machine learning , optics , physics , programming language , operating system
Visible light positioning techniques employing received signal strength (RSS)–based fingerprints are becoming popular and ubiquitous. However, RSS is more susceptible to signal degradation and environmental changes resulting in location inaccuracies. To minimize these limitations, clustering in conjunction with linear regression is applied to RSS database made up of light intensity variations of light emitting diodes. Optimum cluster size is determined and trained clusters are exploited for location assessment by curtailing the difference between the database readings and cluster centroids. Regression is then applied on the clustered data, which partitions it further and helps refining the results. Simulation results of the proposed algorithm dictate a significant improvement in location estimation accuracy of up to 40 cm in an indoor environment with the dimensions of 5 m × 5 m × 4 m and exhibit superior performance than many state‐of‐the‐art RSS‐based methods.