Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

At present, the applications of multiple unmanned aerial vehicles (UAVs) are becoming more and more widespread, covering many civil and military fields. When performing tasks, UAVs will form a flying ad hoc network (FANET) to communicate to each other. However, subject to high mobility, dynamic topo...

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Vydáno v:PloS one Ročník 18; číslo 3; s. e0282333
Hlavní autoři: Chen, Siji, Jiang, Bo, Pang, Tao, Xu, Hong, Gao, Mingke, Ding, Yan, Wang, Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 30.03.2023
Public Library of Science (PLoS)
Témata:
Accuracy
Ad hoc networks
Algorithms
Analysis
Biology and Life Sciences
Cluster Analysis
Clustering
Communication
Computer and Information Sciences
Computer Simulation
Drone aircraft
Earth Sciences
Forecasts and trends
Global positioning systems
GPS
Heuristic methods
Intelligence
Internet of Things
Iterative methods
Light intensity
Localization
Luminous intensity
Maximum likelihood method
Physical Sciences
Research and Analysis Methods
Residual energy
Simulation
Social Sciences
Swarm intelligence
Topology
Unmanned aerial vehicles
China
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:At present, the applications of multiple unmanned aerial vehicles (UAVs) are becoming more and more widespread, covering many civil and military fields. When performing tasks, UAVs will form a flying ad hoc network (FANET) to communicate to each other. However, subject to high mobility, dynamic topology, and limited energy of FANETs, maintaining stable communication performance is a challenging task. As a potential solution, the clustering routing algorithm divides the entire network into multiple clusters to achieve strong network performance. Meanwhile, the accurate localization of UAV is also strongly required when FANETs are applied in the indoor scenario. In this paper, we propose a firefly swarm intelligence based cooperative localization (FSICL) and automatic clustering (FSIAC) for FANETs. Firstly, we combine the firefly algorithm (FA) and Chan algorithm to better cooperative locate the UAVs. Secondly, we propose the fitness function consisting of link survival probability, node degree-difference, average distance, and residual energy, and take it as the light intensity of the firefly. Thirdly, the FA is put forward for cluster-head (CH) selection and cluster formation. Simulation results indicate that the proposed FSICL algorithm achieves the higher localization accuracy faster, and the FSIAC algorithm achieves the higher stability of clusters, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for indoor FANETs.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0282333