A Measurement Study on Edge Computing for Autonomous UAVs
A Measurement Study on Edge Computing for Autonomous UAVs
复制标题
自主无人机边缘计算的测量研究
DOI：
10.1145/3341568.3342109
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Levorato, Marco
中科院分区：
文献类型：
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作者：
Callegaro, Davide;Baidya, Sabur;Levorato, Marco
实时执行复杂信号处理和机器学习任务的能力是自主性的核心。在诸如无人机（UAV）的机载设备中，由重量约束施加的硬件限制使得这些算法的连续执行具有挑战性。边缘和雾计算可以缓解这些限制，并提高无人机的系统和任务级性能。然而，由于无人机的运动特性和城市环境的复杂动力学，使用互连而不是机载资源的管道的性能可能会迅速下降。出于对Hydra的发展，建立灵活的传感，分析，控制管道的自主机载系统的架构，本文报告了一个初步的测量研究计算任务卸载在这类应用程序和系统的可用网络技术的性能。
The ability to execute complex signal processing and machine learning tasks in real-time is the core of autonomy. In airborne devices such as Unmanned Aerial Vehicles (UAV), the hardware limitations imposed by the weight constraint make the continuous execution of these algorithms challenging. Edge and fog computing can mitigate such limitations and boost the system and mission-level performance of the UAVs. However, due to the UAVs motion characteristics and complex dynamics of urban environments, the performance of pipelines using interconnected, rather than onboard, resources can quickly degrade. Motivated by the development of Hydra, an architecture for the establishment of flexible sensing-analysis-control pipelines over autonomous airborne systems, this paper reports a preliminary measurement study on the performance of computing task offloading on available network technologies in this class of applications and systems.