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.
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