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A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

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Title:
A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building.
Authors: 
Rana, Soumya Prakash; Prieto Tejedor, Javier; Dey, Maitreyee; Dudley, Sandra; Corchado Rodríguez, Juan M.
Journal:
Sensors. Volume 18 (11), pp. 1-15. MDPI.

Publication date: 
November 2018
ISSN: 
1424-8220
DOI
 https://doi.org/10.3390/s18113766

BibTex

@article { article,
title = {A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building},
author = {Rana, Soumya Prakash; Prieto Tejedor, Javier; Dey, Maitreyee; Dudley, Sandra; Corchado Rodríguez, Juan M.},
journal = {Sensors},
publisher = {MDPI},
volume = {18},
number = {11},
year = {2018}
}

XML

<article key='journals/Sensors/Rana,/November 2018' mdate='November 2018'>
<author> Rana</author>
<author> Soumya Prakash; Prieto Tejedor</author>
<author> Javier; Dey</author>
<author> Maitreyee; Dudley</author>
<author> Sandra; Corchado Rodríguez</author>
<author> Juan M.</author>
<title> A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building</title>
<pages> 1-15</pages>
<year> 2018</year>
<journal> Sensors</journal>
<ee> https://doi.org/10.3390/s18113766</ee>
</article>
Evidences of quality:
JCR(2017): 2.475
CHEMISTRY, ANALYTICAL: 31/81 (Q2) ELECTROCHEMISTRY: 15/28 (Q3) INSTRUMENTS & INSTRUMENTATION: 16/61 (Q2)

Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

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