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Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0570-0447
Ericsson Research, Laboratoriegränd 11, 977 53 Luleå, Sweden.
Ericsson Research, Laboratoriegränd 11, 977 53 Luleå, Sweden.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 2, article id 864Article in journal (Refereed) Published
Abstract [en]

Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing worldwide. 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.

Place, publisher, year, edition, pages
MDPI, 2023. Vol. 23, no 2, article id 864
Keywords [en]
UAV, 5G, offloading, sensors
National Category
Communication Systems
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-104925DOI: 10.3390/s23020864ISI: 000918867000001PubMedID: 36679660Scopus ID: 2-s2.0-85146702138OAI: oai:DiVA.org:ltu-104925DiVA, id: diva2:1847983
Funder
EU, Horizon 2020, 953454
Note

Godkänd;2024;Nivå 0;2024-04-02 (signyg);

Full text license: CC BY

Available from: 2024-04-01 Created: 2024-04-01 Last updated: 2024-04-19Bibliographically approved
In thesis
1. Towards 5G-Enabled Intelligent Machines
Open this publication in new window or tab >>Towards 5G-Enabled Intelligent Machines
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis introduces a novel framework for enabling intelligent machines and robots with the fifth-generation (5G) cellular network technology. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), Autonomous Guided Vehicles (AGVs), and more, can notably benefit from multi-agent collaboration, human supervision, or operation guidance, as well as from external computational units such as cloud edge servers, in all of which a framework to utilize reliable communication infrastructure is needed. Autonomous robots are often employed to alleviate humans by operating demanding missions such as inspection and data collection in harsh environments or time-critical operations in industrial environments - to name a few. For delivering data to other robots to maximize the effectiveness of the considered mission, for executing complex algorithms by offloading them into the edge cloud, or for including a human operator/supervisor into the loop, the 5G network and its advanced Quality of Service (QoS) features can be employed to facilitate the establishment of such a framework. This work focuses on establishing a baseline for integrating various time-critical robotics platforms and applications with a 5G network. These applications include offloading computationally intensive Model Predictive Control (MPC) algorithms for trajectory tracking of UAVs into the edge cloud, adapting data sharing in multi-robot systems based on network conditions, and enhancing network-aware surrounding autonomy components. We have identified a set of key performance indicators (KPIs) crucially affecting the performance of network-dependent robots and applications. We have proposed novel solutions and mechanisms to meet these requirements, which aim to combine traditional robotics techniques to enhance mission reliability with the exploitation of 5G features such as the QoS framework. Ultimately, our goal was to develop solutions that adhere to the essential paradigm of co-designing robotics with networks. We thoroughly evaluated all presented research using real-life platforms and 5G networks.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2024
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
UAV, 5G, QoS, offloading
National Category
Robotics
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-105110 (URN)978-91-8048-536-4 (ISBN)978-91-8048-537-1 (ISBN)
Presentation
2024-05-29, F341, Luleå University of Technology, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2024-04-16 Created: 2024-04-15 Last updated: 2024-05-08Bibliographically approved

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Damigos, GerasimosNikolakopoulos, George

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