Citation: Cao, P.; Liu, Y.; Yang, C.
Robust Resource Allocation and
Trajectory Planning of UAV-Aided
Mobile Edge Computing in
Post-Disaster Areas. Appl. Sci. 2022,
12, 2226. https://doi.org/10.3390/
app12042226
Academic Editors: Andrzej
Łukaszewicz and Yosoon Choi
Received: 24 January 2022
Accepted: 15 February 2022
Published: 21 February 2022
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Article
Robust Resource Allocation and Trajectory Planning of
UAV-Aided Mobile Edge Computing in Post-Disaster Areas
Peng Cao
1,2
, Yi Liu
1,3,
* and Chao Yang
1,3
1
School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
lhcaopeng_2010@163.com (C.P.); chyang513@gdut.edu.cn (C.Y.)
2
Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China
3
Key Laboratory of Intelligent Detection and Internet of Manufacturing Things, Ministry of Education,
Guangzhou 510006, China
* Correspondence: yi.liu@gdut.edu.cn
Abstract:
When natural disasters strike, users in the disaster area may be isolated and unable
to transmit disaster information to the outside due to the damage of communication facilities.
Unmanned aerial vehicles can be exploited as mobile edge servers to provide emergency service
for ground users due to its mobility and flexibility. In this paper, a robust UAV-aided wireless-
powered mobile edge computing (MEC) system in post disaster areas is proposed, where the UAV
provides charging and computing service for users in the disaster area. Considering the estimation
error of users’ locations, our target is to maximize the energy acquisition of each user by jointly
optimizing the computing offloading process and the UAV trajectory. Due to the strongly coupled
connectionbetween optimization variables and the non-convex nature for trajectory optimization, the
problem is difficult to solve. Furthermore, the semi-infinity of the users’ possible location makes the
problem even more intractable. To tackle these difficulties, we ignore the estimation error of users’
location firstly, and propose an iterative algorithm by using Lagrange dual method and successive
convex approximation (SCA) technology. Then, we propose a cutting-set method to deal with the
uncertainty of users’ location. In this method, we degrade the influence of location uncertainty by
alternating between optimization step and pessimization step. Finally, simulation results show that
the proposed robust algorithm can effectively improve the user energy acquisition.
Keywords:
unmanned aerial vehicle; mobile edge computing; wireless power transfer; trajectory
planning; robust design
1. Introduction
Natural disasters, such as earthquake, flood, and typhoon, often cause huge and
unpredictable losses to human lives and properties [
1
–
3
]. Most of these disasters will result
in unavailability of, or severe damage to, traditional terrestrial wireless infrastructures,
as well as disruption to regional communication, which brings challenges to post-disaster
response and relief [
4
–
6
]. By virtue of the advantages of dynamic mobility, flexibility,
and on-demand deployment, unmanned aerial vehicles (UAVs) have been deemed as a
promising technique in post-disaster area communication recovery [
7
–
9
]. In particular,
the existence of line-of-sight (LoS) links between UAV and ground users has aroused a fast-
growing interest in utilizing UAVs as aerial wireless platforms [
10
–
13
], while the limited
power supply in disaster areas restricts the users’ survival time and equipment performance,
which also puts forward higher requirements for UAV-aided post-disaster services.
To tackle the above mentioned challenge, the combination of mobile edge computing
(MEC) and wireless power transfer (WPT) seems to be an effective approach [
14
–
16
]. On one
hand, by offloading computation tasks to UAVs, users can significantly improve their data
processing capabilities [
17
–
20
]. On the other hand, with the aid of WPT technology, users
can harvest radio-frequency (RF) signals from UAVs to prolong their survival time
[21–23]
.
Appl. Sci. 2022, 12, 2226. https://doi.org/10.3390/app12042226 https://www.mdpi.com/journal/applsci
