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Accurate and Generic Sender Selection for Bulk Data Dissemination in Low-Power Wireless Networks

机译:低功耗无线网络中批量数据分发的准确而通用的发送方选择

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Data dissemination is a fundamental service offered by low-power wireless networks. Sender selection is the key to the dissemination performance and has been extensively studied. Sender impact metric plays a significant role in sender selection, since it determines which senders are selected for transmission. Recent studies have shown that spatial link diversity has a significant impact on the efficiency of broadcast. However, the existing metrics overlook such impact. Besides, they consider only gains but ignore the costs of sender candidates. As a result, existing works cannot achieve accurate estimation of the sender impact. Moreover, they cannot well support data dissemination with network coding, which is commonly used for lossy environments. In this paper, we first propose a novel sender impact metric, namely, γ , which jointly exploits link quality and spatial link diversity to calculate the gain/cost ratio of the sender candidates. Then, we develop a generic sender selection scheme based on the γ metric (called γ -component) that can generally support both types of dissemination using native packets and network coding. Extensive evaluations are conducted through real testbed experiments and large-scale simulations. The performance results and analysis show that γ achieves far more accurate impact estimation than the existing works. In addition, the dissemination protocols based on γ -component outperform the existing protocols in terms of completion time and transmissions (by 20.5% and 23.1%, respectively).
机译:数据分发是低功耗无线网络提供的一项基本服务。发件人的选择是传播性能的关键,并且已被广泛研究。发件人影响度量标准在发件人选择中起着重要作用,因为它确定选择哪些发件人进行传输。最近的研究表明,空间链路分集对广播效率有重大影响。但是,现有指标忽略了这种影响。此外,他们只考虑收益,而忽略寄件人候选人的成本。结果,现有的工作无法实现对发送方影响的准确估计。而且,它们不能很好地支持网络编码的数据分发,而网络编码通常用于有损环境。在本文中,我们首先提出一种新颖的发送方影响度量,即γ,该指标联合利用链路质量和空间链路分集来计算发送方候选者的收益/成本比。然后,我们基于γ度量(称为γ分量)开发一种通用的发件人选择方案,该方案通常可以支持使用本机数据包和网络编码的两种类型的传播。通过真实的试验台实验和大规模模拟进行了广泛的评估。性能结果和分析表明,与现有技术相比,γ可获得更准确的影响估计。此外,基于γ成分的传播协议在完成时间和传输方面均优于现有协议(分别提高了20.5%和23.1%)。

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