Reference : MEC-assisted Dynamic Geofencing for 5G-enabled UAV
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Electrical & electronics engineering
Computational Sciences
http://hdl.handle.net/10993/51202
MEC-assisted Dynamic Geofencing for 5G-enabled UAV
English
Bera, Abhishek mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Space Robotics >]
Sanchez-Cuevas, Pedro J. mailto []
Olivares-Mendez, Miguel Angel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Space Robotics]
16-May-2022
IEEE Wireless Communications and Networking Conference
160-165
Yes
2022 IEEE Wireless Communications and Networking Conference (WCNC)
From10-04-2022 to 13-04-2022
[en] Unmanned Aerial Vehicle ; Mobile Edge Computing ; 5G
[en] 5G-enabled UAV-based services have become popular for civilian applications. At the same time, certain no-fly zones will be highly dynamic, e.g. accident areas, large outdoor public events, VIP convoys etc. An appropriate geofencing algorithm is required to avoid the no-fly zone in such scenarios. However, it is challenging to execute a high computing process such as a geofencing algorithm for a resource constraint UAV. This paper proposes an architecture and a geofencing algorithm for 5G-enabled UAV using Mobile Edge Computing (MEC). Also, the 5G-enabled UAV must fly within the coverage area during a mission. Hence, there must be an optimal trade-off between 5G coverage and distance to travel to design a new trajectory for a 5G-enabled UAV. To this end, we propose a cost minimization problem to generate a new trajectory while a no-fly zone exists. Specifically, we design a cost function considering 5G coverage and the velocity of the UAV. Then, we propose a geofencing algorithm running at the MEC by adopting the fast marching method (FMM) to generate a new trajectory for the UAV. Finally, a numerical example shows how the proposed geofencing algorithm generates an optimal trajectory for a UAV to avoid a dynamically created no-fly zone while on the mission.
University of Luxembourg: Interdisciplinary Centre for Security, Reliability and Trust (SnT)
Department of Media, Telecommunications and Digital Policy, Luxembourg
Mobile Edge Computing for 5G DROne Systems (MICRO5G)
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/51202
10.1109/WCNC51071.2022.9771716
https://ieeexplore.ieee.org/document/9771716
The original publication is available at https://ieeexplore.ieee.org

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