Citation: Yuan, X.; Xie, Z.; Tan, X.
Computation Offloading in
UAV-Enabled Edge Computing:
A Stackelberg Game Approach.
Sensors 2022, 22, 3854. https://
doi.org/10.3390/s22103854
Academic Editors: Diego
González-Aguilera, Pablo
Rodríguez-Gonzálvez and George
Nikolakopoulos
Received: 30 March 2022
Accepted: 17 May 2022
Published: 19 May 2022
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Article
Computation Offloading in UAV-Enabled Edge Computing:
A Stackelberg Game Approach
Xinwang Yuan , Zhidong Xie * and Xin Tan
National Innovation Institute of Defense Technology, Academy of Military Science of the People’s Liberation
Army, Beijing 100071, China; alter_yxw@163.com (X.Y.); tanxin2017@163.com (X.T.)
* Correspondence: xzd313@163.com
Abstract:
This paper studies an efficient computing resource offloading mechanism for UAV-enabled
edge computing. According to the interests of three different roles: base station, UAV, and user,
we comprehensively consider the factors such as time delay, operation, and transmission energy
consumption in a multi-layer game to improve the overall system performance. Firstly, we construct
a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload
allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the
followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the
equilibrium state of the game exists and is unique. Finally, the algorithm’s feasibility is verified by
simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has
certain superiority and robustness.
Keywords:
mobile edge computing; unmanned aerial vehicles; computation offloading; Stackelberg
game
1. Introduction
With the rapid development of network communication technology, the data interac-
tion efficiency of the mobile Internet is constantly improving. Meanwhile, the transmission
bandwidth and data scale have become increasingly large. 5G communication and cloud
computing have spawned new applications such as driverless, automatic navigation, face
recognition, and augmented reality [
1
]. Meanwhile, these applications are computationally
intensive and time-sensitive. However, mobile devices at the terminal cannot provide suffi-
ciently high-performance computing services, and the battery capacity is limited, so it is
inefficient in handling these tasks and may not meet the quality of services
requirements [2].
In the network architecture of cloud computing, computing resources concentrate in the
cloud, and there is a certain distance between the computing resources and terminal de-
vices. Therefore, the service response has an inevitable delay [
3
], and when dealing with
computationally intensive tasks; it is prone to access congestion.
Mobile edge computing (MEC) is a new computing architecture for providing comput-
ing services [
4
,
5
] that can push the service resources of cloud computing to the edge to meet
the requirements of intensive computing and low latency. Compared with cloud computing,
edge computing is more in line with the concept of a smart city, which is proposed to realize
green and sustainable development [
6
]. Traditional terrestrial networks face challenges
in scenarios such as complex terrain and equipment failure. Unmanned aerial vehicles
(UAV) can help to enhance the flexibility and robustness of mobile edge computing network
deployment [
7
], and reduce the complexity and cost of resource management. For example,
Verizon and AWS work together to combine UAVs with mobile edge computing to reduce
connection latency and reduce UAV costs by about 10% [
8
]. However, with the increasing
number of UAVs in use, resource management of networks faces challenges such as power
control, spectrum allocation, interference management, and task allocation [9].
Sensors 2022, 22, 3854. https://doi.org/10.3390/s22103854 https://www.mdpi.com/journal/sensors
