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This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.
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Soft Computing (2022) 26:7479–7492
https://doi.org/10.1007/s00500-021-06465-y
FOCUS
An evolutionary trajectory planning algorithm for multi-UAV-assisted
MEC system
Muhammad Asim1·Wali Khan Mashwani2·Habib Shah3·Samir Brahim Belhaouari4
Accepted: 16 October 2021 / Published online: 1 December 2021
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
Abstract
This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs
are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories
of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their
association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear,
NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA),
which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs.
Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm
is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed
to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA
outperforms other compared algorithms in terms of the energy consumption of the system.
Keywords Mobile edge computing ·Unmanned aerial vehicle ·Evolutionary algorithm ·Genetic algorithm.
1 Introduction
With the development of mobile communication systems,
a huge number of resource-intensive and latency-sensitive
applications are emerging, such as virtual reality, online gam-
ing, and so on. Such applications are usually sensitive to
Communicated by Jia-Bao Liu.
BWali Khan Mashwani
mashwanigr8@gmail.com
Muhammad Asim
asimpk@csu.edu.cn
Habib Shah
habibshah.uthm@gmail.com
Samir Brahim Belhaouari
sbelhaouari@hbku.edu.qa
1School of Computer Science and Engineering, Central South
University, Changsha 410083, China
2Institute of Numerical Sciences, Kohat University of Science
and Technology, Kohat, Pakistan
3College of Computer Science, King Khalid University, Abha,
Saudi Arabia
4College of Science and Engineering, Hamad Bin Khalifa
University, Ar-Rayyan, Qatar
latency and require huge computational resources. However,
due to limitations on mobile users (MUs) devices, it is very
difficult to execute these tasks on them.
Mobile edge computing (MEC) is a promising technology
to address the above-mentioned issue. It can provide services
with low latency and high reliability near or at MUs. It can
execute tasks of MUs at the nearby edge cloud and send back
the results to MUs (Asim et al. 2020). Due to the shorter phys-
ical distance between MEC’s server/edge cloud and MUs, it
consumes less energy as compared to mobile cloud comput-
ing. However, it is still lacking in fulfilling the requirements
of MUs, as the location of the edge cloud is usually fixed and
cannot be adjusted flexibly according to the requirements of
MUs. Therefore, it cannot provide timely services during a
natural disaster as the terrestrial communication link may be
broken/lost.
To satisfy this ever-increasing demand, unmanned aerial
vehicle (UAV) is regarded as one of the most promising tech-
nologies to achieve these ambitious goals. Compared to the
traditional communication systems that utilize the terrestrial
fixed base stations, UAV-aided communication systems are
more cost-effective and likely to achieve a better quality of
service due to their appealing properties of flexible deploy-
ment, fully controllable mobility, and low cost. In fact, with
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