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            <name>Title</name>
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                <text>Book Chapter</text>
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    <name>Book Chapter</name>
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              <text>Faulty Node Detection Using Vertex Magic Total Labelling in Distributed System</text>
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              <text>Adjacency matrix; Clientserver communication; Distributed system; Faulty node; Undirected graph; Vertex magic total labeling</text>
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              <text>Distributed system consists of huge number of nodes that are connected to a network, which is mainly intended and predominantly used for information sharing. Large users are prone to share data through the network and the stability and reliability of the nodes are remaining as the major concern in this system. Therefore, the inconsistent message transmission causes the nodes in the network to act differently, which would not be acceptable. A rapid method of malfunctioning nodes detection can improve the QoS of distributed computing environment. In this paper, a novel algorithm is proposed based on the calculation of vertex magic total labelling (VMTL) value for each and every node in the network. Upon receiving the message from the sender node, the receiver node will quickly detect the faulty node by comparing the VMTL pivot value (Pv). Experimental results show that the proposed approach leads to high true fault rate (TFR) detection accuracy compared to the false fault rate (FFR) detection. Finally, all the information related to the faulty nodes will be sent to the server node for further investigation and action.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.</text>
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          <name>Creator</name>
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              <text>Puthussery A.; Muneeswari G.</text>
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              <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-55, pp. 619-630.</text>
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          <name>Publisher</name>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2021-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-15-8677-4_50" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-15-8677-4_50&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101165609&amp;amp;doi=10.1007%2F978-981-15-8677-4_50&amp;amp;partnerID=40&amp;amp;md5=d21130333863750e399334fe94889c8c" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101165609&amp;amp;doi=10.1007%2f978-981-15-8677-4_50&amp;amp;partnerID=40&amp;amp;md5=d21130333863750e399334fe94889c8c&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 23674512</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Puthussery A., Department of Computational Sciences, School of Science, CHRIST (Deemed to be University), Delhi, India; Muneeswari G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India</text>
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