Paper Info
Title
Performance analysis of clustering-based fingerprinting localization systems
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.
Year
DOI
Venue
2019
10.1007/s11276-018-1682-7
Wireless Networks
Keywords
Field
DocType
Received signal strength (RSS), Fingerprinting localization, Clustering, Searching overhead, Positioning error, Positioning time
Training set,Data mining,Wireless,Computer science,Global Positioning System,Signal strength,Ubiquitous computing,Cluster analysis,RSS,Positioning system,Distributed computing
Journal
Volume
Issue
ISSN
25
5
1572-8196
Citations 
PageRank 
References 
1
0.37
19
Authors
1
Name
Order
Citations
PageRank
Pampa Sadhukhan1162.99