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NDN-CRAHNs: Named Data Networking for Cognitive Radio Ad Hoc Networks

机译:NDN-CRAHNs:认知无线电自组织网络的命名数据网络

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Named data networking (NDN) is a newly proposed paradigm for future Internet, in which communication among nodes is based on data names, decoupling from their locations. In dynamic and self-organized cognitive radio ad hoc networks (CRAHNs), it is difficult to maintain end-to-end connectivity between ad hoc nodes especially in the presence of licensed users and intermittent wireless channels. Moreover, IP-based CRAHNs have several issues like scalability, inefficient-mapping, poor resource utilization, and location dependence. By leveraging the advantages of NDN, in this paper, we propose a new cross layer fine-grained architecture called named data networking for cognitive radio ad hoc networks (NDN-CRAHNs). The proposed architecture provides distinct features such as in-networking caching, security, scalability, and multipath routing. The performances of the proposed scheme are evaluated comparing to IP-based scheme in terms of average end-to-end delay and packet delivery ratio. Simulation results show that the proposed scheme is effective in terms of average contents download time and packet delivery ratios comparing to conventional cognitive radio ad hoc networks.
机译:命名数据网络(NDN)是针对未来Internet的一种新提出的范例,其中节点之间的通信基于数据名称,并与它们的位置分离。在动态的和自组织的认知无线电自组织网络(CRAHN)中,尤其是在存在许可用户和间歇性无线信道的情况下,很难维持自组织节点之间的端到端连接。此外,基于IP的CRAHN具有多个问题,例如可伸缩性,映射效率低,资源利用率低以及位置依赖性。通过利用NDN的优势,在本文中,我们提出了一种新的跨层细粒度体系结构,称为认知无线电自组织网络(NDN-CRAHN)的命名数据网络。所提出的体系结构提供了独特的功能,例如网络内缓存,安全性,可伸缩性和多路径路由。与基于IP的方案相比,在平均端到端延迟和数据包传送率方面评估了所提出方案的性能。仿真结果表明,与传统的认知无线电自组织网络相比,该方案在平均内容下载时间和分组传送率方面是有效的。

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