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
Hlavní autoři: Chen, Siji, Jiang, Bo, Xu, Hong, Ding, Yan, Wang, Xin
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 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.
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
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  givenname: Xin
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  email: 1306182550@qq.com
  organization: School of Communication and Information Engineering, CQUPT,Chongqing,China
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Snippet Subject to high mobility, dynamic topology, and limited energy of unmanned aerial vehicles (UAVs), maintaining stable communication performance is a...
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StartPage 174
SubjectTerms clustering
firefly-tracking
swarm intelligence
UANETs
Title Firefly Swarm Intelligence Based Automatic Clustering and Tracking for UANETs
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