Critical Node Identification Method for Unmanned Aerial Vehicle Cluster Considering Localized Features

doi:10.16182/j.issn1004731x.joss.24-FZ0790

Abstract

Abstract:

Aiming at the problem that the UAV cluster critical node identification methods focus on the global network and ignore the correlation between nodes and their local features, a critical nodes identification method for unmanned aerial vehicle cluster considering local features is proposed. An unmanned aerial vehicle cluster network model is constructed based on complex network theory. The Laplacian energy is introduced to evaluate the importance of node within two hops, and information entropy is combined to evaluate the importance of node in a specific motif to comprehensive identify the critical nodes. Simulation results demonstrate that this method identifies critical nodes that perform well in terms of differentiation, validating its effectiveness. This method exhibits a significant improvement in resilience against continuous failures compared to the comparative methods, further confirming its superiority.

Key words: UAV cluster, critical nodes, local features, Laplacian energy, motif

CLC Number:

TP301

Shi Chenglong, Hua Xiang, Wang Dong, Zhang Jinjin, Jiang Tianqi, Dang Yuanzhang. Critical Node Identification Method for Unmanned Aerial Vehicle Cluster Considering Localized Features[J]. Journal of System Simulation, 2024, 36(12): 2824-2833.

Figures/Tables 9

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Table 1

Unmanned aerial vehicle cluster critical nodes differentiation

算法	$n = 30$		$n = 50$
算法	$t 0$ 时刻	$t 1$ 时刻	$t 0$ 时刻	$t 1$ 时刻
DC	64.37	57.55	64.65	64.39
CC	94.56	97.91	98.37	96.92
EC	100	100	99.84	100
CNILF	97.71	98.17	97.40	99.35

Table 1

Fig. 7

Fig. 8

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