Citation: Damigos, G.; Lindgren, T.;
Sandberg, S.; Nikolakopoulos, G.
Performance of Sensor Data Process
Offloading on 5G-Enabled UAVs.
Sensors 2023, 23, 864. https://
doi.org/10.3390/s23020864
Academic Editors: Xiaochun Cheng
and Daming Shi
Received: 24 December 2022
Revised: 8 January 2023
Accepted: 9 January 2023
Published: 12 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Performance of Sensor Data Process Offloading on
5G-Enabled UAVs
Gerasimos Damigos
1,2,∗
, Tore Lindgren
1
, Sara Sandberg
1
and George Nikolakopoulos
2
1
Ericsson Research, Laboratoriegränd 11, 977 53 Luleå, Sweden
2
Robotics and AI Team, Department of Computer, Electrical and Space Engineering, 971 87 Luleå, Sweden
* Correspondence: gerasimos.damigos@ericsson.com
Abstract:
Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing world-
wide. Thus, there is a strong interest in using UAVs for applications requiring wide-area connectivity
coverage. Such applications might be power line inspection, road inspection, offshore site monitoring,
wind turbine inspections, and others. The utilization of cellular networks, such as the fifth-generation
(5G) technology, is often considered to meet the requirement of wide-area connectivity. This study
quantifies the performance of 5G-enabled UAVs when sensor data throughput requirements are
within the 5G network’s capability and when throughput requirements significantly exceed the
capability of the 5G network, respectively. Our experimental results show that in the first case, the 5G
network maintains bounded latency, and the application behaves as expected. In the latter case, the
overloading of the 5G network results in increased latency, dropped packets, and overall degradation
of the application performance. Our findings show that offloading processes requiring moderate
sensor data rates work well, while transmitting all the raw data generated by the UAV’s sensors is not
possible. This study highlights and experimentally demonstrates the impact of critical parameters
that affect real-life 5G-enabled UAVs that utilize the edge-offloading power of a 5G cellular network.
Keywords: UAV; 5G; offloading; sensors
1. Introduction
Unmanned aerial vehicles (UAVs) are rapidly developing and they are already being
investigated and chosen for a wide range of applications. The unique mobility, site reach-
ability, and emerging ability to interact with the environment have constituted UAVs to
be at the center of research for academic institutions and various organizations [
1
,
2
]. One
central point of the current discussions around UAV applications revolves around use cases
that require wide-area connectivity [
3
,
4
]. With wide-area connectivity, UAV applications
will be less dependent on infrastructure and may even be automated for recurrent assign-
ments. As such, the possibility for automation and infrastructure-free deployments opens
a vast field of applications regarding UAVs as a sensing platform. Power line inspection,
road inspections, security surveillance, wind power plant inspection, and other potential
applications could be some of the most notable ones [5].
Some of the most important benefits that the aforementioned applications will expe-
rience revolve around the capabilities of the UAV sensing platform. Cellular-connected
UAVs can access the extensive computational power of the edge cloud and can be assisted
by centralized processing schemes while maintaining constant connectivity [
4
,
6
]. This
potential for centralized intelligence makes it possible to handle numerous UAVs or UAV
swarms as an interconnected system and perform various types of centralized data process-
ing, constituting the most traditional definition of cloud robotics. The edge cloud promises
vast improvements in the real-time execution of computationally intensive algorithms,
such as object detection and others [
7
]. As a result, instead of always being outfitted with
powerful computational resources, UAVs can now be developed with fewer computational
Sensors 2023, 23, 864. https://doi.org/10.3390/s23020864 https://www.mdpi.com/journal/sensors
