<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462020000200835</article-id>
<article-id pub-id-type="doi">10.13053/cys-24-2-3122</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[K-Medoids Clustering Based Next Location Prediction in Wireless Local Area Network]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Majumder]]></surname>
<given-names><![CDATA[Abhishek]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chakraborty]]></surname>
<given-names><![CDATA[Sukanta]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chakraborty]]></surname>
<given-names><![CDATA[Ashmita]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Begam]]></surname>
<given-names><![CDATA[Selina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tripura University Department of Computer Science and Engineering ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>India</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<volume>24</volume>
<numero>2</numero>
<fpage>835</fpage>
<lpage>844</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462020000200835&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462020000200835&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462020000200835&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: User mobility prediction in wireless network is being investigated from various angles to improve performance of the network. Access to user&#8217;s movement information such as time, direction, speed, etc. provides an opportunity for wireless networks to manage effectively resources to satisfy user needs. A next location prediction technique is required for transferring the existing connections of user to the next Access Point (AP) beforehand to ensure better Quality of Service (QoS) of the network. There are several techniques for next location prediction of mobile users in Wireless Local Area Network (WLAN), which include Indoor Next Location Prediction with Wi-Fi model, Extended Mobility Markov Chain Model, Hidden Markov Model and Mixed Membership Stochastic Blockmodel. In the Indoor Next Location Prediction with Wi-Fi model, the area of prediction is fixed and small which makes this approach inefficient when the number of locations traversed by the mobile user is large. The paper addresses the issue of predicting the next location of mobile users in a WLAN when the area of prediction is vast. In this paper, an intelligent clustering technique i.e., the K-Medoids clustering algorithm has been implemented on the indoor next location prediction, which is based on a Markov-chain model, for predicting the next location of a user when the number of locations traversed by the user is vast. The accuracy of prediction of mobile user&#8217;s next location by the proposed K-Medoids clustering based next location prediction technique ranges from 67% to 97%.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Wireless local area network]]></kwd>
<kwd lng="en"><![CDATA[next location prediction]]></kwd>
<kwd lng="en"><![CDATA[Markov chain]]></kwd>
<kwd lng="en"><![CDATA[Quality of Service]]></kwd>
<kwd lng="en"><![CDATA[K-medoids clustering]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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