TY - JOUR
T1 - Critical Node Identification of Multi-UUV Formation Based on Network Structure Entropy
AU - Chen, Yi
AU - Liu, Lu
AU - Zhang, Xiaomeng
AU - Qiao, Wei
AU - Ren, Ranzhen
AU - Zhu, Boyu
AU - Zhang, Lichuan
AU - Pan, Guang
AU - Yu, Yang
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/8
Y1 - 2023/8
N2 - In order to identify and attack the multi-UUV (unmanned underwater vehicle) groups, this paper proposes a method for identifying the critical nodes of multi-UUV formations. This method helps in combating multi-UUV formations by identifying the key nodes to attack them. Moreover, these multi-UUV formations are considered to have an unknown structure as the research object. Therefore, the network structure of the formation is reconstructed according to its space–time trajectory, and the importance of nodes is determined based on network structure entropy. As for the methodology, firstly, based on the swarm intelligence behavior method, the motion similarity of multi-UUV nodes in the formation is analyzed in pairs; furthermore, the leader–follower relationship and the network structure of the formation are calculated successively. Then, based on this network structure, the importance of the network nodes is further determined by the network structure entropy method. Finally, through simulation and experiments, it is verified that the algorithm can accurately construct the network structure of the unknown multi-UUV formation, and the accuracy of the calculated time delay data reaches 84.6%, and compared with the traditional information entropy algorithm, the ordering of the important nodes obtained by this algorithm is more in line with the underwater formation network.
AB - In order to identify and attack the multi-UUV (unmanned underwater vehicle) groups, this paper proposes a method for identifying the critical nodes of multi-UUV formations. This method helps in combating multi-UUV formations by identifying the key nodes to attack them. Moreover, these multi-UUV formations are considered to have an unknown structure as the research object. Therefore, the network structure of the formation is reconstructed according to its space–time trajectory, and the importance of nodes is determined based on network structure entropy. As for the methodology, firstly, based on the swarm intelligence behavior method, the motion similarity of multi-UUV nodes in the formation is analyzed in pairs; furthermore, the leader–follower relationship and the network structure of the formation are calculated successively. Then, based on this network structure, the importance of the network nodes is further determined by the network structure entropy method. Finally, through simulation and experiments, it is verified that the algorithm can accurately construct the network structure of the unknown multi-UUV formation, and the accuracy of the calculated time delay data reaches 84.6%, and compared with the traditional information entropy algorithm, the ordering of the important nodes obtained by this algorithm is more in line with the underwater formation network.
KW - critical node
KW - formation identification
KW - multi-UUV formation
KW - network reconstruction
KW - network structural entropy
UR - http://www.scopus.com/inward/record.url?scp=85168919094&partnerID=8YFLogxK
U2 - 10.3390/jmse11081538
DO - 10.3390/jmse11081538
M3 - 文章
AN - SCOPUS:85168919094
SN - 2077-1312
VL - 11
JO - Journal of Marine Science and Engineering
JF - Journal of Marine Science and Engineering
IS - 8
M1 - 1538
ER -