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              <text>Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs</text>
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              <text>Multi-hop wireless sensor networks; multi-server multi-class queuing models; multiway relay node; network coding; throughput</text>
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              <text>In todays information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results.  2023 Tech Science Press. All rights reserved.</text>
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              <text>Menaria V.K.; Nayyar A.; Kumar S.; Kotecha K.</text>
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              <text>Computers, Materials and Continua, Vol-74, No. 1, pp. 1199-1216.</text>
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              <text>Tech Science Press</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.32604/cmc.2023.032162" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.32604/cmc.2023.032162&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139753367&amp;amp;doi=10.32604%2Fcmc.2023.032162&amp;amp;partnerID=40&amp;amp;md5=b4488f31221890e1ea2d0d23f3e597db" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139753367&amp;amp;doi=10.32604%2fcmc.2023.032162&amp;amp;partnerID=40&amp;amp;md5=b4488f31221890e1ea2d0d23f3e597db&lt;/a&gt;</text>
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 15462218</text>
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              <text>Menaria V.K., Department of IT &amp;amp; C (DoIT &amp;amp; C), Govt of Rajasthan, Jaipur, India; Nayyar A., Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, Viet Nam; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Kotecha K., Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India</text>
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