Accurate and Generic Sender Selection for Bulk Data Dissemination in Low-Power Wireless Networks
Accurate and Generic Sender Selection for Bulk Data Dissemination in Low-Power Wireless Networks
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DOI：
10.1109/tnet.2016.2614129
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Zhiwei Zhao;Wei Dong;Jiajun Bu;Tao Gu;G. Min
中科院分区：
文献类型：
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作者：
Zhiwei Zhao;Wei Dong;Jiajun Bu;Tao Gu;G. Min
数据分发是低功率无线网络提供的基本服务。传播性能的关键是传播路径的选择，这一问题已经得到了广泛的研究。报文影响度量在发送方选择中起着重要的作用，因为它决定了选择哪些报文进行传输。最近的研究表明，空间链路分集广播的效率有显着的影响。然而，现有的衡量标准忽略了这种影响。此外，他们只考虑收益，而忽略了发送者候选人的成本。因此，现有的作品不能实现发送者影响的准确估计。此外，它们不能很好地支持网络编码的数据传播，而网络编码通常用于有损环境。在本文中，我们首先提出了一种新的发送方影响度量，即$\gamma $，它联合利用链路质量和空间链路分集来计算发送方候选者的增益/成本比。然后，我们开发了一个通用的发送者选择方案的基础上的$\gamma $度量（称为$\gamma $ -组件），一般可以支持这两种类型的传播使用本地数据包和网络编码。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，$\gamma $实现了更准确的影响估计比现有的作品。此外，基于$\gamma $ -组件的传播协议在完成时间和传输方面优于现有协议（分别为20.5%和23.1%）。
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, $\gamma $ , 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 $\gamma $ metric (called $\gamma $ -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 $\gamma $ achieves far more accurate impact estimation than the existing works. In addition, the dissemination protocols based on $\gamma $ -component outperform the existing protocols in terms of completion time and transmissions (by 20.5% and 23.1%, respectively).