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            <name>Title</name>
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    <name>Article</name>
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              <text>An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks</text>
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          <name>Subject</name>
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              <text>Clustering; Mobile ad hoc network; Network lifetime; Route maintenance; Routing protocol; Star topology</text>
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              <text>Mobile ad hoc network (MANET) is arranged with multiple nodes that communicate wirelessly. However, MANET communication suffers from various issues such as inadequate security, low stability, high power consumption, and a lack of specific infrastructure of the network. Moreover, the route failure happened in the network due to the unrestricted node movement, which has increased energy utilization, delay, and reduced lifetime of the nodes. To overcome these issues, the novel Eagle Based Density Clustering (EBDC) approach is developed in this research that predicts the link failure and increased the lifetime of the nodes. Here, the developed EBDC approach is utilized for clustering and route maintenance in MANET for that it creates the nodes using the star topology. Initially, the developed approach selects the Cluster Head and transmits the message through the created path. Subsequently, the link failure is detected by the EBDC model, and it creates a new reference layer to replace the exhausted layer. Hence, the developed EBDC model has enhanced the network lifetime and reduced energy utilization. Furthermore, this model is implemented using Network Simulator 2, and the parameters like accuracy, energy consumption, Packet Delivery Ratio, network lifetime, end-to-end delay, and throughput are calculated. Additionally, the attained outcomes are compared with prevailing methods for evaluating the efficiency of the developed approach.  2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</text>
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          <name>Creator</name>
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              <text>Vatambeti R.; Sanshi S.; Krishna D.P.</text>
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              <text>Journal of Ambient Intelligence and Humanized Computing, Vol-14, No. 1, pp. 305-319.</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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          <name>Date</name>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/s12652-021-03298-3" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s12652-021-03298-3&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105447125&amp;amp;doi=10.1007%2Fs12652-021-03298-3&amp;amp;partnerID=40&amp;amp;md5=80fefcc73425c56f297c4d60643b7eb0" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105447125&amp;amp;doi=10.1007%2fs12652-021-03298-3&amp;amp;partnerID=40&amp;amp;md5=80fefcc73425c56f297c4d60643b7eb0&lt;/a&gt;</text>
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              <text>ISSN: 18685137</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Article</text>
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              <text>Vatambeti R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Sanshi S., Department of Information Science and Engineering, BMS Institute of Technology &amp;amp; Management, Bangalore, India; Krishna D.P., Department of Computer Science and Engineering, Narayana Engineering College, Andhra Pradesh, Guduru, India</text>
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