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Infective flooding in low-duty-cycle networks, properties and bounds

机译:低占空比网络,属性和界限的感染洪水

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摘要

Flooding information is an important function in many networking applications. In some networks, as wireless sensor networks or some ad-hoc networks it is so essential as to dominate the performance of the entire system. Exploiting some recent results based on the distributed computation of the eigenvector centrality of nodes in the network graph and classical dynamic diffusion models on graphs, this paper derives a novel theoretical framework for efficient resource allocation to flood information in mesh networks with low duty-cycling without the need to build a distribution tree or any other distribution overlay. Furthermore, the method requires only local computations based on each node neighborhood. The model provides lower and upper stochastic bounds on the flooding delay averages on all possible sources with high probability. We show that the lower bound is very close to the theoretical optimum. A simulation-based implementation allows the study of specific topologies and graph models as well as scheduling heuristics and packet losses. Simulation experiments show that simple protocols based on our resource allocation strategy can easily achieve results that are very close to the theoretical minimum obtained building optimized overlays on the network.
机译:洪水信息是许多网络应用中的重要功能。在某些网络中,作为无线传感器网络或某些ad-hoc网络,它非常重要,以占据整个系统的性能。利用基于网络图表和古典动态扩散模型的节点的分布式计算的最新结果,在图形上的经典动态扩散模型中,推导了一种新的理论框架,用于高效资源分配对具有低循环的网状网络中的洪水信息而没有需要构建分发树或任何其他分发覆盖。此外,该方法仅需要基于每个节点邻域的本地计算。该模型在洪水延迟平均值上提供了较低和上部随机界限,所有可能的源具有高概率。我们表明下限非常接近理论最优。基于仿真的实现允许研究特定拓扑和图形模型以及调度启发式和数据包丢失。仿真实验表明,基于我们的资源分配策略的简单协议可以很容易地实现非常接近网络上的理论最小值的结果,该结果是网络上的优化叠加。

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