Chimera: exploiting UAS flight path information to optimize heterogeneous data transmission

Russell Shirey, Sanjay G. Rao, S. Sundaram
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引用次数: 1

Abstract

Unmanned Aerial Systems (UAS) collect and transmit data such as live video and radar images, which have different latency and reliability requirements, over wireless links that exhibit much performance variability. In this paper, we make three contributions. First, we show through a characterization of two real-world UAS flight datasets that there is significant opportunity to optimize data transmission in UAS settings by exploiting knowledge of UAS flight paths. Second, we developed Chimera, a system that taps into this opportunity while transmitting heterogeneous data streams over UAS networks. Chimera learns a model online that relates UAS network throughput to the flight path, and combines the model with a control framework that optimizes transmissions based on long-range throughput prediction. Third, with a combination of emulation and simulation experiments using real-world flight traces, we show Chimera’s effectiveness. Specifically, Chimera reduces penalties related to dropped radar images by 72.4%−100% compared to an algorithm agnostic to flight path information, and achieves an average bitrate of 90.5% compared to an optimal scheme that knows the exact future throughput, with only a minimal increase in radar images dropped.
嵌合体:利用无人机航路信息优化异构数据传输
无人机系统(UAS)通过无线链路收集和传输数据,如实时视频和雷达图像,这些数据具有不同的延迟和可靠性要求,表现出很大的性能变化。在本文中,我们做了三个贡献。首先,我们通过两个真实世界的UAS飞行数据集的特征显示,通过利用UAS飞行路径的知识,在UAS设置中存在优化数据传输的重大机会。其次,我们开发了Chimera,这是一个利用这一机会在无人机网络上传输异构数据流的系统。Chimera在线学习了一个将无人机网络吞吐量与飞行路径联系起来的模型,并将该模型与基于远程吞吐量预测优化传输的控制框架相结合。第三,结合使用真实飞行轨迹的仿真和仿真实验,我们展示了奇美拉的有效性。具体来说,与航迹信息不可知的算法相比,Chimera减少了与丢失雷达图像相关的惩罚72.4% - 100%,与知道确切未来吞吐量的最优方案相比,平均比特率达到90.5%,雷达图像丢失的增加很小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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