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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 30, Number 10(1) (2018)
Copyright(C) MYU K.K.
pp. 2221-2233
S&M1671 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2018.1844
Published: October 12, 2018

Soft-clustering Technique for Fingerprint-based Localization [PDF]

Panarat Cherntanomwong and Pitikhate Sooraksa

(Received December 9, 2017; Accepted May 16, 2018)

Keywords: localization, fingerprint technique, soft clustering, wireless sensor network, ZigBee

In this paper, the soft-clustering algorithm for the fingerprint-based localization technique is proposed. In an indoor environment, the fingerprint-based localization technique is usually employed since it can deal with signal fluctuation. Its basic principle is to find the target location by comparing its signal parameters with a previously recorded database of known-location-signal parameters. Here, the received signal strength indicator (RSSI) provided by the wireless sensor network (WSN) is used as the signal parameter. The high accuracy of location estimation requires a very fine spatial resolution of the database, corresponding to the time consumed for pattern matching. To reduce the calculation time, clustering can be applied because it can reduce the database size by grouping similar data in the same cluster. The accuracy of the algorithm to cluster the target location and fingerprint locations is the main concern. The result shows that the clustering technique used can successfully cluster the target sensing node into an appropriate cluster. This implies that, by using soft clustering with the fingerprint technique, the target location can be estimated faster than by using classical fingerprint techniques since the target location can be estimated within a small set of fingerprints in the cluster, not with all fingerprints in the database.

Corresponding author: Panarat Cherntanomwong


Cite this article
Panarat Cherntanomwong and Pitikhate Sooraksa, Soft-clustering Technique for Fingerprint-based Localization, Sens. Mater., Vol. 30, No. 10, 2018, p. 2221-2233.



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