RT Journal Article
T1 Is the edge really necessary for drone computing offloading? An experimental assessment in carrier‐grade 5G operator networks
A1 Candal Ventureira, David
A1 González Castaño, Francisco Javier
A1 Gil Castiñeira, Felipe Jose
A1 Fondo Ferreiro, Pablo
K1 3325.99 Otras
AB 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.
PB Software Practice and Experience
SN 00380644
YR 2023
FD 2023-03
LK http://hdl.handle.net/11093/4620
UL http://hdl.handle.net/11093/4620
LA eng
NO Software Practice and Experience, 53(3): 579-599 (2023)
NO Ministerio de Ciencia e Innovación | Ref. PDC2021‐121335‐C21
DS Investigo
RD 03-dic-2023