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首页> 外文期刊>International journal of communication systems >Differential evolution CapsNet model for QoS routing enhancement in wireless networks
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Differential evolution CapsNet model for QoS routing enhancement in wireless networks

机译:无线网络中QoS路由增强的差分演进谱模型

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摘要

One of the nondeterministic polynomial (NP) hard problem in the area of wireless networks is the quality of service (QoS) routing problem. This has always been a key area wherein research is required to enhance the QoS metrics considered with respect to the considered network model. In this paper, mobile ad hoc network (MANET) model is identified for which an effective QoS routing protocol which meets the set QoS constraints is designed employing the novel variant of differential evolution (DE) model. The ultimate aim of the proposed work is to identify an optimal feasible path for the MANET model with the set objective function criterion being met. A variant of DE capsule net model is built by hybridizing it with the nature-inspired firefly (FF) algorithmic approach. The effectiveness of the proposed technique is achieved by minimizing the set objective function on satisfying the QoS constraints in the MANETs. The developed technique chose better QoS path with respect to the best fitness value than that of the route replay the shortest path algorithm of ad hoc on-demand distance vector (AODV) protocol. The modeled variant DE-FF model achieves better QoS metrics with all the set constrained being met. Simulated results depict the superiority and better performance of the developed DE-FF model than that of the other methods considered for comparison from the existing literature works.
机译:无线网络领域的非确定性多项式(NP)难题之一是服务质量(QoS)路由问题。这一般是一个关键区域,其中需要研究以增强关于所考虑的网络模型考虑的QoS度量。在本文中,识别移动临时网络(MANET)模型,其中符合符合集QoS约束的有效QoS路由协议,采用差分演进(DE)模型的新型变型设计。拟议工作的最终目标是识别MANET模型的最佳可行路径,其中满足了集合目标函数标准。通过将其与自然启发萤火虫(FF)算法方法杂交,构建了De胶囊净型号的变体。通过最小化满足舰队中的QoS限制的设定的目标函数来实现所提出的技术的有效性。开发技术选择了与Route Replay的最佳选择值相对于最佳的QoS路径的QoS路径,重放临时按需距离矢量(AODV)协议的最短路径算法。模型变体DE-FF模型与所满足的所有集合实现更好的QoS度量。模拟结果描绘了显影的DE-FF模型的优越性和更好的性能,而不是考虑与现有文献作品进行比较的其他方法的优势和更好的性能。

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