DyCOCo: A Dynamic Computation Offloading and Control Framework for Drone Video Analytics
DyCOCo: A Dynamic Computation Offloading and Control Framework for Drone Video Analytics
复制标题
DOI：
10.1109/icnp.2019.8888089
复制
发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
Chengyi Qu;Songjie Wang;P. Calyam
中科院分区：
文献类型：
--
作者：
Chengyi Qu;Songjie Wang;P. Calyam
配备摄像头的无人机或无人机系统广泛应用于不同的监控场景，通常需要实时控制和高质量的视频传输。然而，不稳定的网络情况和各种传输协议可能导致视频流传输期间的损害，这进而对用户的体验质量（QoE）产生负面影响。在本文中，我们提出了一个动态的计算卸载和控制框架，名为DyCOCo，在各种可用的网络带宽条件下的图像损伤检测的基础上。我们的DyCOCo框架演示在GENI基础设施上的测试台设置中展示了物联网设备。我们的演示结果表明，我们的DyCOCo方法可以有效地选择合适的网络协议，并协调无人机上的摄像头控制以及有限边缘计算/网络资源上的视频分析计算卸载。
Unmanned aerial vehicles (UAV) or drone systems equipped with cameras are extensively used in different surveillance scenarios and often require real-time control and highquality video transmission. However, unstable network situations and various transport protocols may result in impairments during video streaming, which in turn negatively impacts user’s quality of experience (QoE). In this paper, we propose a dynamic computation offloading and control framework, named DyCOCo, based on image impairment detection under various available network bandwith conditions. Our DyCOCo framework demo features IoT devices in a testbed setup on the GENI infrastructure. Our demo results show that our DyCOCo approach can efficiently choose the suitable networking protocols and orchestrate both the camera control on the drone, and the computation offloading of the video analytics over limited edge computing/networking resources.