Intelligence-Empowered Modeling, Control, and Optimization of Complex Networked Systems

Dear Colleagues,

Complex networked systems have become one of the most research hotspots and are widely applied to various fields such as electronics, power grids, transportation networks, communication networks, and social networks. It is promising to use advanced computational techniques and artificial intelligence (AI) to develop models, control strategies, and optimization algorithms for complex systems consisting of interconnected components or nodes. These techniques leverage data-driven approaches, machine learning algorithms, and optimization methods to analyze system behavior, predict outcomes, and optimize system performance.

It is a challenge to model the structure of complex systems to obtain more dynamics and a better understanding of their complex topology and functional behavior. This renders the network approach especially well suited for investigating crucial facets of complexity. When facing attacks and/or failures, the ability of complex networked systems to maintain their resilience and functionality is a prominent issue in the scientific and engineering fields. The goal is to establish a mathematical theory to elucidate efficient control strategies for exploring security, robustness, and optimization issues in complex networked systems.

This Special Issue focuses on the challenges and problems in intelligence-empowered modeling, control, and optimization of complex networked systems. This topic aims to share and discuss recent advances and future trends of information processing, network security, and privacy-preserving for complex networked systems. It is expected to attract widespread attention and will have an excellent impact on the field of network science.

The topics of interest include, but are not limited to, the following:

1. Machine learning techniques for system identification and state estimation;
2. Data-driven modeling approaches for complex and uncertain networked systems;
3. AI-based fault diagnosis and attack detection towards complex and high-dimensional networked systems;
4. Fuzzy logic and neural network-based robust and adaptive controllers for distributed networked systems;
5. AI-empowered optimization algorithms for resource allocation and control in complex networked systems;
6. Multi-objective optimization in autonomous vehicles and intelligent transportation systems;
7. Integrating 5G and beyond, edge, and cloud computing in complex networked systems;
8. Security and privacy techniques in intelligent control systems;
9. Innovative sensing and actuation technologies for data processing and data fusion;
10. Intelligent scheduling and management in networked control systems.

Prof. Dr. Jinliang Liu
Dr. Yan Li
Dr. Jian Liu
Guest Editors

Manuscript Submission Information

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• networked systems
• networked control
• intelligent control
• security control
• AI-empowered optimization