Firefly Swarm Intelligence Based Automatic Clustering and Tracking for UANETs
Subject to high mobility, dynamic topology, and limited energy of unmanned aerial vehicles (UAVs), maintaining stable communication performance is a challenging task in UAV ad-hoc networks (UANETs). As a potential solution, clustering routing algorithm divides the entire network into multiple cluste...
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| Vydáno v: | 2022 6th International Conference on Communication and Information Systems (ICCIS) s. 174 - 178 |
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14.10.2022
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| Abstract | Subject to high mobility, dynamic topology, and limited energy of unmanned aerial vehicles (UAVs), maintaining stable communication performance is a challenging task in UAV ad-hoc networks (UANETs). As a potential solution, clustering routing algorithm divides the entire network into multiple clusters and various optimal strategies can be adopted to achieve strong network performance. In this paper, we propose a firefly swarm intelligence based automatic clustering and tracking algorithm (FSIACT) for UANETs, which is inspired by the collective behavior of fireflies. Firstly, we propose the fitness function consisting of link survival possibility, average distance and residual energy, and utilize it as the light intensity of the firefly. Secondly, firefly algorithm (FA) is put forward for cluster head (CH) selection and cluster management. Based on the characteristics of the FA, the whole swarm can be automatically divided into several clusters and cluster members (CMs) are willing to track the CH in the cluster. It is verified in simulations that the proposed algorithm achieves the lower handover rate of CHs, longer link expiration time (LET) and longer node lifetime. |
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| AbstractList | Subject to high mobility, dynamic topology, and limited energy of unmanned aerial vehicles (UAVs), maintaining stable communication performance is a challenging task in UAV ad-hoc networks (UANETs). As a potential solution, clustering routing algorithm divides the entire network into multiple clusters and various optimal strategies can be adopted to achieve strong network performance. In this paper, we propose a firefly swarm intelligence based automatic clustering and tracking algorithm (FSIACT) for UANETs, which is inspired by the collective behavior of fireflies. Firstly, we propose the fitness function consisting of link survival possibility, average distance and residual energy, and utilize it as the light intensity of the firefly. Secondly, firefly algorithm (FA) is put forward for cluster head (CH) selection and cluster management. Based on the characteristics of the FA, the whole swarm can be automatically divided into several clusters and cluster members (CMs) are willing to track the CH in the cluster. It is verified in simulations that the proposed algorithm achieves the lower handover rate of CHs, longer link expiration time (LET) and longer node lifetime. |
| Author | Jiang, Bo Ding, Yan Xu, Hong Wang, Xin Chen, Siji |
| Author_xml | – sequence: 1 givenname: Siji surname: Chen fullname: Chen, Siji email: cqchensj@foxmail.com organization: Chongqing University of Posts and Telecommunications (CQUPT),School of Computer Science and Technology,Chongqing,China – sequence: 2 givenname: Bo surname: Jiang fullname: Jiang, Bo email: b26jiang@126.com organization: The 32nd Research Institute of China Electronics Technology Group Corporation,Shanghai,China – sequence: 3 givenname: Hong surname: Xu fullname: Xu, Hong email: frankxuh@126.com organization: The 32nd Research Institute of China Electronics Technology Group Corporation,Shanghai,China – sequence: 4 givenname: Yan surname: Ding fullname: Ding, Yan email: dingyan0052@hotmail.com organization: The 32nd Research Institute of China Electronics Technology Group Corporation,Shanghai,China – sequence: 5 givenname: Xin surname: Wang fullname: Wang, Xin email: 1306182550@qq.com organization: School of Communication and Information Engineering, CQUPT,Chongqing,China |
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| SubjectTerms | clustering firefly-tracking swarm intelligence UANETs |
| Title | Firefly Swarm Intelligence Based Automatic Clustering and Tracking for UANETs |
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