优化布谷鸟搜索算法提高认知无线电自组织网络服务路由质量

Ramahlapane Lerato Moila, M. Velempini, Miyelani Silence Madiba
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引用次数: 1

摘要

云计算、移动性和物联网(IoT)创造了大量数据集,这需要更多的频谱来传输数据。不幸的是,频谱是一种稀缺资源,授权用户未充分利用频谱,而未授权频谱则过度拥挤。认知无线电技术已成为解决频谱利用不足的一种有希望的解决方案。本文的研究重点在于减少信号开销,提高服务质量(QoS)和路由协议的可扩展性,以确保网络的可靠性。利用带有认知无线网络补丁的网络模拟器2.31在Linux操作系统上对该方案进行了仿真。该研究使用了一种被称为布谷鸟搜索算法的元启发式算法,该算法受到大自然的启发,建立了一个有足够资源的稳定路径。从文献中,研究人员确定了在自组织网络中可以在延迟,丢包和QoS方面实现良好路由性能的技术。仿真结果表明,该方案的性能优于现有方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimisation of Cuckoo Search Algorithm to improve Quality of Service Routing in Cognitive Radio Ad Hoc Networks
Cloud computing, mobility, and the Internet of Things (IoT) have created large data sets, which require more spectrum for data transmission. Unfortunately, the spectrum is a scarce resource, which is being underutilised by licensed users while the unlicensed spectrum is overcrowded. Cognitive radio technology has emerged as a promising solution to the underutilization of the spectrum. This study, focuses on minimizing the signal overhead, improving of Quality of Service (QoS) and the scalability of routing protocols to ensure the reliability of the network. Network simulator 2.31 with a cognitive radio network patch was used to simulate the proposed scheme in Linux operating system. The study uses a metaheuristic algorithm called cuckoo search algorithm inspired by nature to establish a stable path with enough resources. From the literature, researchers identified techniques, which can achieve good performance in routing in terms of delay, dropped packets and QoS in ad hoc networks. The simulations depict that the proposed scheme performs better than the existing scheme.
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