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              <text>Congestion Avoidance in Vehicular Ad Hoc Network MAC Layer Using Harmony SearchModified Laying Chicken Algorithm (HS-MLCA)</text>
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              <text>Congestion avoidance; Harmony search; HS-MLCA; MAC; VANET</text>
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              <text>To address congestion in the MAC layer and enhance overall performance, the HS-MLCA is proposed. This algorithm incorporates the principles of both Harmony Search and Laying Chicken Algorithm to optimize resource allocation and congestion control. At the MAC layer, HS-MLCA offers several advantages over traditional congestion control schemes. Firstly, it leverages the Harmony Search algorithm, which is known for its ability to exploit the best outcomes in search processes. By exploring the solution space and exploiting promising regions, HS-MLCA optimizes resource allocation in the MAC layer. The integration of the Laying Chicken Algorithm (LCA) further enhances performance by improving convergence speed and solution accuracy. This hybrid approach leverages the strengths of both Harmony Search (HS) and LCA, resulting in more efficient and effective resource management. The Laying Chicken Algorithm simulates the behavior of laying hens in terms of resource allocation and competition. This approach contributes to provide the solution in quality and convergence speed, as the algorithm adapts to the dynamic nature of the MAC layer and the varying traffic conditions in VANETs. By combining the strengths of Harmony Search and Laying Chicken Algorithm, HS-MLCA offers improved performance in terms of congestion control in the MAC layer. It optimizes resource allocation, minimizes collisions and packet loss, reduces delay, and enhances overall network efficiency. These improvements ultimately lead to better quality of service, increased network capacity, and enhanced user experience in VANETs. It is worth noting that the specific performance improvements and benefits of HS-MLCA may vary depending on the implementation details, network conditions, and the specific VANET scenario.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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              <text>Manimekala B.; Prabha S.P.; Suresh S.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-1030 LNNS, pp. 161-174.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-97-3745-1_14" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-97-3745-1_14&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210597606&amp;amp;doi=10.1007%2F978-981-97-3745-1_14&amp;amp;partnerID=40&amp;amp;md5=a104b596e1d8c7ca4cfc5100867faf2f" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210597606&amp;amp;doi=10.1007%2f978-981-97-3745-1_14&amp;amp;partnerID=40&amp;amp;md5=a104b596e1d8c7ca4cfc5100867faf2f&lt;/a&gt;</text>
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              <text>ISSN: 23673370; ISBN: 978-981973744-4</text>
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              <text>Manimekala B., Department of Computer Science, Christ University, Bengaluru, India; Prabha S.P., Department of Computer Applications, Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India; Suresh S., Department of Computer Applications, Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India</text>
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