Chimera: exploiting UAS flight path information to optimize heterogeneous data transmission
Chimera: exploiting UAS flight path information to optimize heterogeneous data transmission
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DOI：
10.1109/icnp52444.2021.9651923
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Russell Shirey;Sanjay G. Rao;S. Sundaram
中科院分区：
文献类型：
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作者：
Russell Shirey;Sanjay G. Rao;S. Sundaram
无人驾驶航空系统(UAS)通过表现出很大性能变化的无线链路收集和传输实时视频和雷达图像等数据，这些数据具有不同的延迟和可靠性要求。在本文中，我们做出了三点贡献。首先，我们通过对两个真实的UAS飞行数据集的描述表明，通过利用UAS飞行路径的知识来优化UAS环境中的数据传输具有重要的机会。其次，我们开发了Chimera，这是一个利用这个机会通过UAS网络传输不同类型数据流的系统。Chimera在线学习了一个将UAS网络吞吐量与飞行路线联系起来的模型，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，通过仿真和使用真实世界飞行轨迹的模拟实验相结合，我们展示了Chimera的有效性。具体地说，与不知道飞行路径信息的算法相比，Chimera将与丢失的雷达图像相关的惩罚减少了72.4%−100%，与知道准确的未来吞吐量的最优方案相比，平均比特率达到90.5%，而丢失的雷达图像只有很小的增加。
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.