Is the edge really necessary for drone computing offloading? An experimental assessment in carrier-grade 5G operator networks
arxiv(2024)
摘要
In this article, we evaluate the first experience of computation offloading
from drones to real fifth-generation (5G) operator systems, including
commercial and private carrier-grade 5G networks. A follow-me drone service was
implemented as a representative testbed of remote video analytics. In this
application, an image of a person from a drone camera is processed at the edge,
and image tracking displacements are translated into positioning commands that
are sent back to the drone, so that the drone keeps the camera focused on the
person at all times. The application is characterised to identify the
processing and communication contributions to service delay. Then, we evaluate
the latency of the application in a real non standalone 5G operator network, a
standalone carrier-grade 5G private network, and, to compare these results with
previous research, a Wi-Fi wireless local area network. We considered both
multi-access edge computing (MEC) and cloud offloading scenarios. Onboard
computing was also evaluated to assess the trade-offs with task offloading. The
results determine the network configurations that are feasible for the
follow-me application use case depending on the mobility of the end user, and
to what extent MEC is advantageous over a state-of-the-art cloud service.
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