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Wireless Networks
Erschienen in: Wireless Networks 5/2019

08.02.2018

Performance analysis of clustering-based fingerprinting localization systems

verfasst von: Pampa Sadhukhan

Erschienen in: Wireless Networks | Ausgabe 5/2019

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Abstract

Localization is highly required to develop the smart-phone based pervasive computing applications. Because of very poor signal strength of global positioning system in indoor areas, various indoor localization systems have been proposed in literature. Among these, received signal strength (RSS) based fingerprinting localization systems are very popular. However, these localization systems at first, need to construct a fingerprint database by collecting RSS patterns at a set of known training locations and then determine the location of an object by comparing the currently observed RSS pattern with all the RSS patterns stored in the fingerprint database. Thus, such localization systems can provide better positioning accuracy by including large number of training data, which in turn, increase the searching overhead. To resolve this issue, several clustering strategies, which restrict the search within a smaller subset of the whole fingerprint database for such localization systems, have been proposed in the literature over the past decade. This paper presents an extensive comparative performance analysis of various clustering-based fingerprinting localization systems to demonstrate their effectiveness on the large-scale positioning system in the presence of radio irregularities and wall attenuation in the wireless environment.
Vorheriger Artikel Throughput analysis of wireless-powered decode-and-forward relay systems with interference
Nächster Artikel A practical sleep coordination and management scheme with duty cycle control for energy sustainable IEEE 802.11s wireless mesh networks

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Metadaten
Titel
Performance analysis of clustering-based fingerprinting localization systems
verfasst von
Pampa Sadhukhan
Publikationsdatum
08.02.2018
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 5/2019
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-018-1682-7

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