Optimising the Cuckoo Search Algorithm for Improved Quality of Service in Cognitive Radio ad hoc Networks

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2023-08-03 DOI:10.1109/icABCD59051.2023.10220569

Ramahlapane Lerato Moila, M. Velempini

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引用次数: 0

Abstract

This study proposes an optimised routing scheme, called OCS-AODV, for Cognitive Radio Ad Hoc Networks (CRAHNs) to enhance Quality of Service (QoS). The scheme applies the Cuckoo Search (CS) algorithm optimised with a fitness function to improve the performance of the Ad Hoc On-Demand Distance Vector (AODV). The objective of the study is to evaluate the proposed scheme's performance with respect to delay, packet loss, packet delivery ratio and throughput. The literature review shows that the existing routing protocols have limitations which impact performance in dynamic environments. The proposed OCS-AODV scheme aims to address these limitations by selecting reliable paths based on a fitness function that considers the lifetime of nodes, reliability, and available buffer capacity. The simulation results have shown that the OCS-AODV scheme outperforms the CS-DSDV and ACO-AODV schemes in terms of PDR, packet loss, delay, and throughput. The study concludes that the proposed scheme improves the QoS of routing in CRAHNs. However, the use of a single fitness function may not be optimal for all network scenarios. Multiple fitness functions may be considered in future and the schemes be evaluated in real-world CRAHNs

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优化布谷鸟搜索算法以提高认知无线电自组织网络的服务质量

本研究提出了一种优化的路由方案，称为OCS-AODV，用于认知无线电自组织网络(CRAHNs)，以提高服务质量(QoS)。该方案采用适应度函数优化的布谷鸟搜索(CS)算法来提高Ad Hoc按需距离矢量(AODV)的性能。研究的目的是评估所提出的方案在延迟、丢包、包传送率和吞吐量方面的性能。文献综述表明，现有的路由协议存在局限性，影响动态环境下的性能。提出的OCS-AODV方案旨在通过基于考虑节点生存期、可靠性和可用缓冲容量的适应度函数选择可靠路径来解决这些限制。仿真结果表明，OCS-AODV方案在PDR、丢包、时延和吞吐量方面都优于CS-DSDV和ACO-AODV方案。研究表明，该方案提高了crahn中路由的QoS。然而，对于所有网络场景，使用单一适应度函数可能不是最优的。未来可以考虑多个适应度函数，并在实际的crahn中对方案进行评估

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.