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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| Abstract | 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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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Qin, Chunbin Li, Zhijia Zhang, Lingli Xia, Xuewen Yan, Yonghang |
| AuthorAffiliation | 1 School of Computer and Information Engineering, Henan University, Kaifeng 475004, China 3 Beijing Aerospace Automatic Control Institute, Beijing 100854, China 4 School of Artificial Intelligence, Henan University, Kaifeng 475004, China 2 Henan Province Engineering Research Center of Spatial Information Processing, Henan University, Kaifeng 475004, China |
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| Author_xml | – sequence: 1 givenname: Yonghang orcidid: 0000-0002-8606-793X surname: Yan fullname: Yan, Yonghang – sequence: 2 givenname: Xuewen orcidid: 0000-0003-4241-0553 surname: Xia fullname: Xia, Xuewen – sequence: 3 givenname: Lingli surname: Zhang fullname: Zhang, Lingli – sequence: 4 givenname: Zhijia orcidid: 0000-0003-1472-5718 surname: Li fullname: Li, Zhijia – sequence: 5 givenname: Chunbin orcidid: 0000-0002-8238-5922 surname: Qin fullname: Qin, Chunbin |
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| Cites_doi | 10.1109/ACCESS.2021.3124710 10.1109/ACCESS.2021.3130417 10.3390/s22093236 10.1007/s13042-017-0731-3 10.1007/s11432-019-2984-7 10.1109/MWC.2018.1800160 10.1109/ACCESS.2019.2902940 10.1016/j.advengsoft.2016.01.008 10.5055/jem.0496 10.1109/TVT.2020.2973294 10.1109/MSPEC.2017.7802742 10.1109/ACCESS.2019.2955993 10.1109/ACCESS.2020.3000222 10.1109/COMST.2020.2982452 10.1109/ICC.2017.7996485 10.1109/EIT51626.2021.9491898 10.1007/s11432-018-9579-3 10.1109/JIOT.2019.2925567 10.1109/ACCESS.2021.3053605 10.3390/s18051413 10.1109/TWC.2014.2337315 |
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| SubjectTerms | 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 |
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| Title | A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET |
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