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    崔建群, 孙佳悦, 常亚楠, 余东海, 邬尧, 吴黎兵. DTN中基于节点综合性能的自适应喷射等待路由算法[J]. 计算机研究与发展, 2022, 59(4): 852-863. DOI: 10.7544/issn1000-1239.20200976
    引用本文: 崔建群, 孙佳悦, 常亚楠, 余东海, 邬尧, 吴黎兵. DTN中基于节点综合性能的自适应喷射等待路由算法[J]. 计算机研究与发展, 2022, 59(4): 852-863. DOI: 10.7544/issn1000-1239.20200976
    Cui Jianqun, Sun Jiayue, Chang Yanan, Yu Donghai, Wu Yao, Wu Libing. An Adaptive Spray and Wait Routing Algorithm Based on Comprehensive Performance of Node in DTN[J]. Journal of Computer Research and Development, 2022, 59(4): 852-863. DOI: 10.7544/issn1000-1239.20200976
    Citation: Cui Jianqun, Sun Jiayue, Chang Yanan, Yu Donghai, Wu Yao, Wu Libing. An Adaptive Spray and Wait Routing Algorithm Based on Comprehensive Performance of Node in DTN[J]. Journal of Computer Research and Development, 2022, 59(4): 852-863. DOI: 10.7544/issn1000-1239.20200976

    DTN中基于节点综合性能的自适应喷射等待路由算法

    An Adaptive Spray and Wait Routing Algorithm Based on Comprehensive Performance of Node in DTN

    • 摘要: 延迟容忍网络(delay tolerant network, DTN)中,由于网络拓扑频繁变化,端到端之间不存在稳定的链路,如何选择合适的中继节点进行消息转发,使消息在较短时间内交付到目标节点是DTN中研究的关键问题之一.针对现有路由算法中继节点选择的盲目性以及对消息副本的分发缺乏合理控制的问题,提出一种基于节点综合性能的自适应喷射等待路由算法(adaptive spray and wait routing algorithm based on comprehensive performance of node, CPN-ASW):在Spray(喷射)阶段引入节点相似度指标来衡量节点间运动轨迹的相似程度,根据节点相似度是否超过给定阈值采用不同的中继节点选择策略,确定中继节点后,按照节点相对效用值自适应分配消息副本数量;在Wait(等待)阶段实现主动转发,将消息转发给到目标节点投递预测值更高的中继节点.实验结果表明,与Epidemic,Spray and Wait (SaW),EBR,PBSW这4种算法相比,CPN-ASW算法能够有效提高消息投递率,降低网络开销和平均时延.

       

      Abstract: In delay tolerant network (DTN), due to the frequent changes of network topology, there is no stable link between the end to end, so it is one of the key problems in DTN research to select the appropriate relay node for message forwarding and delivering the message to the destination node in a short time. Aiming at the blindness of relay node selection and the lack of reasonable control over message copies distribution in existing routing algorithms, an adaptive spray and wait routing algorithm based on comprehensive performance of node (CPN-ASW) is proposed: in spray phase, a new metric, called as node similarity, is used to measure the similarity degree of motion trajectory between nodes, and different relay node selection strategies are adopted according to whether the node similarity exceeds the given threshold value, and subsequently the number of message copies are adaptively allocated according to the relative utility value of nodes; in wait phase, an active forwarding strategy based on delivery probability is implemented, if a relay node has a lager delivery probability to the destination node, forwarding the message to this relay node. Simulation results show that compared with the Epidemic, Spray and Wait (SaW), EBR and PBSW, CPN-ASW can effectively control the network overhead while improving the delivery ratio and reducing the average delay.

       

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