MEC-assisted Dynamic Geofencing for 5G-enabled UAV

• Published in: 2022 IEEE Wireless Communications and Networking Conference (WCNC) pp. 160 - 165
• Main Authors: Bera, Abhishek, Sanchez-Cuevas, Pedro J., Olivares-Mendez, Miguel Angel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.04.2022
• Subjects: 5G mobile communication; Autonomous aerial vehicles; Computer architecture; Cost function; Costs; Fast Marching Method (FMM); Geofence; Heuristic algorithms; Minimization; Mobile Edge Computing (MEC); Unmanned Aerial Vehicle (UAV)
• ISSN: 1558-2612
• DOI: 10.1109/WCNC51071.2022.9771716

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.