首页|A survey on the network models applied in the industrial network optimization

A survey on the network models applied in the industrial network optimization

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Network architecture design is critical for optimizing industrial networks.Network architectures can be classified into small-scale networks and large-scale networks based on scale.Graph theory is an efficient mathematical tool for network topology modeling.For small-scale networks,their structure often has regular topology.For large-scale ones,the current body of work mainly focuses on random characteristics of network nodes and edges.Recently,widely used models include random networks,small-world networks,and scale-free networks.In this study,starting from the scale of the network,network modeling methods based on graph theory as well as their industrial applications,are summarized and analyzed.Moreover,a novel network performance metric,called system entropy,is proposed.From the perspective of mathematical properties,an analysis of its non-negativity and concavity is performed.The advantage of system entropy is that it can cover the existing regular networks,random networks,small-world networks,and scale-free networks,and has strong generality.The simulation results reveal that this proposed metric can achieve the comparison of various industrial networks under different models.

industrial networksmall-scale networklarge-scale networkgraph theorysystem entropy

Chao DONG、Xiaoxiong XIONG、Qiulin XUE、Zhengzhen ZHANG、Kai NIU、Ping ZHANG

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Key Laboratory of Universal Wireless Communications,Ministry of Education,Beijing University of Posts and Telecommunications,Beijing 100876,China

Smart City College,Beijing Union University,Beijing 100024,China

Key Program of National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaKey Laboratory of Universal Wireless Communications(BUPT)Ministry of Education,China

9206720262071058KFKT-2022104

2024

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子:0.715
ISSN:1674-733X
年,卷(期):2024.67(2)
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