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

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 1289

Special Issue Editors


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Guest Editor
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: networked control systems; cyber-physical systems; time-delay systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
Interests: networked-contronl systems; cyber-physical systems; complex networks
Special Issues, Collections and Topics in MDPI journals
College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
Interests: cyber-physical systems; complex networks; fuzzy control; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

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Keywords

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

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Published Papers (2 papers)

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Research

18 pages, 5339 KiB  
Article
A Modeling Method for Emergency Rescue Center Siting Based on the Variable Butterfly Optimization Algorithm
by Yibo Sun, Lei Yue, Huihui Jin, Weitong Chen and Zhe Sun
Electronics 2025, 14(8), 1606; https://doi.org/10.3390/electronics14081606 - 16 Apr 2025
Viewed by 175
Abstract
Selecting appropriate locations of emergency centers is an important issue in avoiding probable damages by natural disasters. Emergency rescue sites are constructed to provide emergency supplies swiftly for people in affected areas. Factors of transportation fluency and road damage degrees should be considered, [...] Read more.
Selecting appropriate locations of emergency centers is an important issue in avoiding probable damages by natural disasters. Emergency rescue sites are constructed to provide emergency supplies swiftly for people in affected areas. Factors of transportation fluency and road damage degrees should be considered, which largely affect rescue efficiency. In order to find appropriate sites accurately, we proposed a redesigned method Variable Butterfly Optimization Algorithm (VBOA), based on the Butterfly Optimization Algorithm, by adding the Variation Operator mechanism to avoid the limitations of local optimum problems present in other optimization algorithms. The Variation Operator effectively combines both global and local search strategies to improve the performance of global searching, and it accelerates the convergence speed of the algorithm. We conducted our experiment on selected candidate sites with multiple optimization methods; the experiment results demonstrate that our proposed method maintains the balance between conditions of coverage area and expenditure. Our proposed method relieved the reliance of local optimum results and achieved better convergence accuracy in our selected samples in comparison with other methods both in initial and later siting phases. Full article
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19 pages, 6361 KiB  
Article
Investigating Intelligent Call Technology for Dispatching Telephones Towards System Integration
by Chunliang Tai, Yibo Sun, Shiming Sun, Zhixin Sun, Xing Chen, Yue Shi and Chao Liu
Electronics 2025, 14(1), 179; https://doi.org/10.3390/electronics14010179 - 4 Jan 2025
Viewed by 722
Abstract
The dispatching telephone functionality acts as a pivotal interconnection between the power grid dispatch business and telecommunications business, playing a vital role in ensuring the efficient conduct of grid dispatch activities. Nonetheless, the current power grid dispatch system and communication program-controlled exchange system [...] Read more.
The dispatching telephone functionality acts as a pivotal interconnection between the power grid dispatch business and telecommunications business, playing a vital role in ensuring the efficient conduct of grid dispatch activities. Nonetheless, the current power grid dispatch system and communication program-controlled exchange system are disjointed, leading to a cumbersome process for the dispatching telephone functionality that severely impacts grid dispatch efficiency. To better tackle the above challenges, in this paper, we introduce an innovative intelligent call technology designed to facilitate data interchange and information integration between the power grid dispatch system and the communication program-controlled exchange system. By leveraging the K-Nearest Neighbors (KNN) algorithm, the technology enables automated querying of operational information with heightened efficiency and precision, thereby optimizing the operations of the dispatching telephone functionality. Subsequently, a prototype software application is developed to conduct experimental testing of intelligent call technology. The findings demonstrate that the method proposed in this paper successfully reduces the time expenditure associated with the dispatching telephone functionality, enhancing the productivity of dispatchers in routine operations and emergency response, thus ensuring the secure and stable operation of the power grid. Full article
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