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Smoggy-Link: Fingerprinting interference for predictable wireless concurrency

机译:Smoggy-Link:指纹干扰可预测的无线并发

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Operating in unlicensed ISM bands, ZigBee devices often yield poor throughput and packet reception ratio due to the interference from ever increasing wireless devices in 2.4 GHz band. Although there have been many efforts made for interference avoidance, they come at the cost of miscellaneous overhead, which oppositely hurts channel utilization. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to describe and trace the relationship between interference and link quality of the sender's outbound links. With such a link model, Smoggy-Link can obtain fine-grained spatiotemporal link information through a low-cost interference identification method. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferer.
机译:ZigBee设备在未经许可的ISM频段中运行,由于2.4 GHz频段中不断增长的无线设备的干扰,经常会产生较差的吞吐量和数据包接收率。尽管人们为避免干扰做出了许多努力,但它们是以杂项开销为代价的,这反过来会损害信道利用率。我们的经验结果表明,特定的干扰可能会对ZigBee发送方的不同出站链路产生不同的影响,这表明并发传输的机会。基于此见解,我们提出了Smoggy-Link,这是一种实用协议,可在恶劣干扰下利用自适应ZigBee传输的潜在并发性。 Smoggy-Link维护一个准确的链路模型,以描述和跟踪干扰与发送方出站链路的链路质量之间的关系。利用这种链接模型,Smoggy-Link可以通过低成本的干扰识别方法获得细粒度的时空链接信息。链路信息还用于自适应链路选择和智能传输调度。我们使用TinyOS和TelosB Motes实现并评估了我们的方法的原型。评估结果表明,在各种干扰源的干扰下,Smoggy-Link在吞吐量和数据包接收率方面均具有一致的提高。

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