BICSF: Bio-Inspired Clustering Scheme for FANETs
Flying ad hoc networks (FANETs) have dynamic topology because of the mobile unmanned aerial vehicles (UAVs). The limited battery resource and mobility of UAVs cause unstable routing in the FANET. In this paper, we try to minimize this issue with the help of an efficient clustering scheme. We propose...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8664645/ |
| _version_ | 1818913108034322432 |
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| author | Ali Khan Farooq Aftab Zhongshan Zhang |
| author_facet | Ali Khan Farooq Aftab Zhongshan Zhang |
| author_sort | Ali Khan |
| collection | DOAJ |
| description | Flying ad hoc networks (FANETs) have dynamic topology because of the mobile unmanned aerial vehicles (UAVs). The limited battery resource and mobility of UAVs cause unstable routing in the FANET. In this paper, we try to minimize this issue with the help of an efficient clustering scheme. We propose a bio-inspired clustering scheme for FANETs (BICSF), which uses the hybrid mechanism of glowworm swarm optimization (GSO) and krill herd (KH). The proposed scheme uses energy aware cluster formation and cluster head election on the basis of the GSO algorithm. Furthermore, we propose an efficient cluster management algorithm using the behavioral study of KH. We also use genetic operators such as mutation and crossover for the optimal position of the UAV. For route selection, we propose a path detection function based on the weighted residual energy, number of neighbors, and distance between the UAVs for efficient communication. The performance of BICSF is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with grey wolf optimization and ant colony optimization-based clustering algorithms. |
| first_indexed | 2024-12-19T23:25:14Z |
| format | Article |
| id | doaj.art-1b55503b24154460bde44facd7d31e5a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:25:14Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1b55503b24154460bde44facd7d31e5a2022-12-21T20:01:52ZengIEEEIEEE Access2169-35362019-01-017314463145610.1109/ACCESS.2019.29029408664645BICSF: Bio-Inspired Clustering Scheme for FANETsAli Khan0https://orcid.org/0000-0002-2695-3307Farooq Aftab1https://orcid.org/0000-0002-2939-466XZhongshan Zhang2School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaFlying ad hoc networks (FANETs) have dynamic topology because of the mobile unmanned aerial vehicles (UAVs). The limited battery resource and mobility of UAVs cause unstable routing in the FANET. In this paper, we try to minimize this issue with the help of an efficient clustering scheme. We propose a bio-inspired clustering scheme for FANETs (BICSF), which uses the hybrid mechanism of glowworm swarm optimization (GSO) and krill herd (KH). The proposed scheme uses energy aware cluster formation and cluster head election on the basis of the GSO algorithm. Furthermore, we propose an efficient cluster management algorithm using the behavioral study of KH. We also use genetic operators such as mutation and crossover for the optimal position of the UAV. For route selection, we propose a path detection function based on the weighted residual energy, number of neighbors, and distance between the UAVs for efficient communication. The performance of BICSF is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with grey wolf optimization and ant colony optimization-based clustering algorithms.https://ieeexplore.ieee.org/document/8664645/FANETbio-inspiredself-organizationclusteringenergy optimizationrouting |
| spellingShingle | Ali Khan Farooq Aftab Zhongshan Zhang BICSF: Bio-Inspired Clustering Scheme for FANETs IEEE Access FANET bio-inspired self-organization clustering energy optimization routing |
| title | BICSF: Bio-Inspired Clustering Scheme for FANETs |
| title_full | BICSF: Bio-Inspired Clustering Scheme for FANETs |
| title_fullStr | BICSF: Bio-Inspired Clustering Scheme for FANETs |
| title_full_unstemmed | BICSF: Bio-Inspired Clustering Scheme for FANETs |
| title_short | BICSF: Bio-Inspired Clustering Scheme for FANETs |
| title_sort | bicsf bio inspired clustering scheme for fanets |
| topic | FANET bio-inspired self-organization clustering energy optimization routing |
| url | https://ieeexplore.ieee.org/document/8664645/ |
| work_keys_str_mv | AT alikhan bicsfbioinspiredclusteringschemeforfanets AT farooqaftab bicsfbioinspiredclusteringschemeforfanets AT zhongshanzhang bicsfbioinspiredclusteringschemeforfanets |