Citation: Yang, X.; Yu, T.; Chen, Z.;
Yang, J.; Hu, J.; Wu, Y. An Improved
Weighted and Location-Based
Clustering Scheme for Flying Ad Hoc
Networks. Sensors 2022, 22, 3236.
https://doi.org/10.3390/s22093236
Academic Editor: Carlos
Tavares Calafate
Received: 19 March 2022
Accepted: 21 April 2022
Published: 22 April 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
An Improved Weighted and Location-Based Clustering Scheme
for Flying Ad Hoc Networks
Xinwei Yang
1
, Tianqi Yu
1
, Zhongyue Chen
1
, Jianfeng Yang
1,
*, Jianling Hu
1,2
and Yingrui Wu
1
1
School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China;
20204228021@stu.suda.edu.cn (X.Y.); tqyu@suda.edu.cn (T.Y.); chenzy@suda.edu.cn (Z.C.);
jlhu@suda.edu.cn (J.H.); 1928401099@stu.suda.edu.cn (Y.W.)
2
School of Electronic and Information Engineering, Wuxi University, Wuxi 214105, China
* Correspondence: jfyang@suda.edu.cn
Abstract:
Flying ad hoc networks (FANETs) have been gradually deployed in diverse application
scenarios, ranging from civilian to military. However, the high-speed mobility of unmanned aerial
vehicles (UAVs) and dynamically changing topology has led to critical challenges for the stability
of communications in FANETs. To overcome the technical challenges, an Improved Weighted and
Location-based Clustering (IWLC) scheme is proposed for FANET performance enhancement, under
the constraints of network resources. Specifically, a location-based K-means++ clustering algorithm
is first developed to set up the initial UAV clusters. Subsequently, a weighted summation-based
cluster head selection algorithm is proposed. In the algorithm, the remaining energy ratio, adaptive
node degree, relative mobility, and average distance are adopted as the selection criteria, considering
the influence of different physical factors. Moreover, an efficient cluster maintenance algorithm is
proposed to keep updating the UAV clusters. The simulation results indicate that the proposed IWLC
scheme significantly enhances the performance of the packet delivery ratio, network lifetime, cluster
head changing ratio, and energy consumption, compared to the benchmark clustering methods in
the literature.
Keywords:
unmanned aerial vehicle (UAV); K-means++ clustering; cluster head selection; cluster
maintenance; flying ad hoc network (FANET)
1. Introduction
Unmanned aerial vehicles (UAVs) have been pervasively used in civilian and mili-
tary fields, such as collaborative formation, mission reconnaissance, precision agriculture,
material distribution, and environmental monitoring [
1
]. However, the computational
and communication capabilities of a single UAV cannot meet the increasing requirements
of such applications [
2
,
3
]. Additionally, because of the rapid development of wireless
communication technology, the miniaturization, intelligence, and networking of UAVs
have become a research trend [4].
Under such a situation, flying ad hoc networks (FANETs), a new research field of
ad hoc networks, have developed as a promising networking paradigm. FANETs share
properties with mobile ad hoc networks (MANETs) and their sub-classes, such as vehicular
ad hoc networks (VANETs) and wireless sensor networks (WSNs). However, FANETs have
the features of high mobility, scalability, three-dimensional (3D) deployment, and frequent
topology changes. Furthermore, UAVs as the network nodes are capable of transmitting
information, exchanging data packets, and automatically establishing a wireless network
in the air. The velocity and density of UAVs are greater than other ad hoc networks.
These features can lead to the instability of UAV swarms, which makes it difficult to design
a stable and effective scheme for FANETs [5].
The network communications can be affected by several issues, including unstable
link connections between UAVs, limited communication range between the ground control
Sensors 2022, 22, 3236. https://doi.org/10.3390/s22093236 https://www.mdpi.com/journal/sensors
