Modeling Dual-Layer Interdependent Command and Control Networks for Integrated Reconnaissance-Strike and OODA-Loop Capabilities
In the context of information warfare, command and control (C2) networks are exhibiting increasingly prominent multi-network dependency characteristics, leading to a growing interest in the study of interdependent C2 network models. We propose a modeling approach for interdependent C2 networks based...
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Published in | IEEE transactions on network science and engineering pp. 1 - 16 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
IEEE
13.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | In the context of information warfare, command and control (C2) networks are exhibiting increasingly prominent multi-network dependency characteristics, leading to a growing interest in the study of interdependent C2 network models. We propose a modeling approach for interdependent C2 networks based on integrated reconnaissance-strike and OODA loop, addressing the limitations of existing edge connection strategies in effectively capturing the interdependent coupling relationships within the network. First, the interdependent relationships within the network are described, and a dual-layer structural model of interdependent C2 networks is abstracted. Second, based on the local efficiency of nodes, an edge connection strategy for sensing and firepower nodes under integrated reconnaissance-strike is proposed. Third, interdependence strength and link balance are defined, and an inter-layer coupling edge connection strategy based on interdependence strength and link balance is proposed. Finally, algorithmic simulations are designed to analyze the network properties of the model and network performance under different edge connection strategies. Simulation results demonstrate that the proposed modeling method effectively captures the interdependent characteristics of C2 networks while exhibiting enhanced network resilience against destruction. |
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ISSN: | 2327-4697 2334-329X |
DOI: | 10.1109/TNSE.2024.3443191 |