A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize n...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 24; no. 10; p. 1366
Main Authors: Yan, Yonghang, Xia, Xuewen, Zhang, Lingli, Li, Zhijia, Qin, Chunbin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 27.09.2022
MDPI
Subjects:
Ad hoc networks
Ad hoc networks (Computer networks)
Algorithms
Analysis
binary whale optimization algorithm (BWOA)
Clustering
Clustering (Computers)
Communication
Drone aircraft
Energy consumption
Energy resources
Energy sources
FANET
Intelligence
Maintenance
Management
Mathematical optimization
Methods
Optimization
Optimization algorithms
Surveillance
Technology application
UAV clusters
Unmanned aerial vehicles
China
UAV clusters
clustering
FANET
binary whale optimization algorithm (BWOA)
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e24101366