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            <name>Title</name>
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    <name>Article</name>
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          <name>Title</name>
          <description>A name given to the resource</description>
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              <text>Minimizing Energy Depletion Using Extended Lifespan: QoS Satisfied Multiple Learned Rate (ELQSSM-ML) for Increased Lifespan of Mobile Adhoc Networks (MANET)</text>
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          <name>Subject</name>
          <description>The topic of the resource</description>
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              <text>energy depletion; MANET; multicast communication; network lifetime path stability; QSSM-ML algorithm</text>
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          <name>Description</name>
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              <text>Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the nodes vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the networks lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the nodes energy use for broadcasting the data from the origin to the target. Moreover, the nodes energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms.  2023 by the authors.</text>
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          <name>Creator</name>
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              <text>Rajagopal M.; Sivasakthivel R.; Venugopal J.; Sarris I.E.; Loganathan K.</text>
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          <name>Source</name>
          <description>A related resource from which the described resource is derived</description>
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              <text>Information (Switzerland), Vol-14, No. 4</text>
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          <name>Publisher</name>
          <description>An entity responsible for making the resource available</description>
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              <text>MDPI</text>
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          <name>Date</name>
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            <elementText elementTextId="97151">
              <text>2023-01-01</text>
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          <name>Identifier</name>
          <description>An unambiguous reference to the resource within a given context</description>
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              <text>&lt;a href="https://doi.org/10.3390/info14040244" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.3390/info14040244&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153675883&amp;amp;doi=10.3390%2Finfo14040244&amp;amp;partnerID=40&amp;amp;md5=7cbcb1aedffbdbe4b7eaa4fffff795c8" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153675883&amp;amp;doi=10.3390%2finfo14040244&amp;amp;partnerID=40&amp;amp;md5=7cbcb1aedffbdbe4b7eaa4fffff795c8&lt;/a&gt;</text>
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          <name>Rights</name>
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              <text>All Open Access; Gold Open Access</text>
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          <name>Relation</name>
          <description>A related resource</description>
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              <text>ISSN: 20782489</text>
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          <name>Format</name>
          <description>The file format, physical medium, or dimensions of the resource</description>
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              <text>Online</text>
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          <name>Language</name>
          <description>A language of the resource</description>
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            <elementText elementTextId="97156">
              <text>English</text>
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          <name>Type</name>
          <description>The nature or genre of the resource</description>
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              <text>Article</text>
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          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <text>Rajagopal M., Department of Lean Operations and Systems, School of Business and Management, CHRIST, Karnataka, Bengaluru, 560001, India; Sivasakthivel R., Department of Computer Science, School of Sciences, CHRIST, Karnataka, Bengaluru, 560001, India; Venugopal J., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Sarris I.E., Department of Mechanical Engineering, University of West Attica, 250 Thivon &amp;amp; P. Ralli Str, Athens, Egaleo, 12244, Greece; Loganathan K., Department of Mathematics and Statistics, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India</text>
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