Program


1. Invited Session Title: Distributed Control and Optimization of Networking Systems

Invited Session Chairs:

Chair: Wenwu Yu, Southeast University; wwyu@seu.edu.cn

Co-chairs: Guanghui Wen, Southeast University, RMIT University; wenguanghui@gmail.com

                He Wang, Southeast University; wanghe@seu.edu.cn

Distributed control and optimization of networking systems (including multi-agent systems and complex networks) has received much attention from different scientific communities in the last ten years. A critical question on networking systems is how the collective behaviors can be emerged as a result of local interaction and how some global optimization problem can be solved by performing distributed optimization solvers. Understanding the fundamental mechanism responsible for the emergence of collective behaviors and regulating these collective behaviors have important impacts on infrastructure networks such as the Internet and power systems. The main focus of this invited session will be on the new approaches for distributed control and optimization of networking systems as well as their potential applications in networked engineering systems. Suitable topics include, but are not limited to, the following:

1. Synchronization of complex dynamical networks
2. Consensus of multi-agent systems
3. Distributed optimization of networking systems
4. Distributed filter design of large-scale sensor networks
5. Event-triggered-based protocol design
6. Distributed fault-tolerant control
7. Distributed estimation
8. Distributed anti-disturbance control
9. Distributed control and estimation in smart grids
10. Distributed prediction control in networked agent systems

2. Invited Session Title: Network-based method and applications

Invited Session Chairs:

Chair: Hong-xiang Hu, Hangzhou Dianzi University; kukunan911@hotmail.com

Co-chairs: Guang Chen, Taizhou University; xcgmsn@163.com

                Ying Wan, Southeast University; wanying1991seu@gmail.com

Many real-world large-scale systems can be modeled as distributed intelligent systems, where examples include distributed sensor systems, a team of robots, complex social systems, and so on. Recently, analysis and synthesis of distributed intelligent systems have found extensive applications in various domains including social computing, information diffusion, community detection, formation control, spacecraft control, distributed sensor networks, and smart grids. Since each individual agent (individual) in the distributed intelligent systems has limited computational and sensing abilities, distributed control and estimation design have more significant potential advantages than centralized ones in the context of distributed intelligent systems. Furthermore, it has been shown that the abundance of embedded computational and sensing resources in distributed intelligent systems enables enhanced operational effectiveness through cooperative teamwork in real applications. The main focus of this invited session will be on new and existing distributed analysis and synthesis approaches in distributed intelligent systems, which will certainly become an international forum for researchers in all branches of applied mathematics, social science, control engineering, as well as computer science to present, share, and summarize the most recent developments and ideas on related topics. Topics of the session include but are not limited to, the following:

1. Consensus, flocking, and swarming control of networked intelligent systems
2. Modeling, identification, and optimization of networked intelligent systems
3. Robust control of distributed intelligent systems
4. Distributed diagnosis and fault-tolerant control
5. Distributed neural network-based control
6. Community detection in complex networks
7. Network analysis and data mining in complex social systems
8. Information diffusion on social networks

3. Invited Session Title: Complex Cyber-Physical Networks: Analysis and Synthesis

Invited Session Chairs:

Chair: Guanghui Wen, Southeast University, RMIT University; wenguanghui@gmail.com

Co-chairs: Zhiwei Liu, Huazhong University of Science and Technology; zwliu@sina.com

                Chaojie Li, RMIT University; cjlee.cqu@163.com

Complex cyber-physical network refers to a new generation of complex networks whose normal functioning significantly relies on tight interactions between its physical and cyber components. Many modern critical infrastructures can be appropriately modelled as complex cyber-physical networks. Typical examples of such infrastructures are electrical power grids, WWW, public transportation systems, state financial networks, and the Internet. These critical facilities play important roles in ensuring the stability of society as well as the development of economy. This invited session is focused on analysis and synthesis of complex cyber-physical networks. Topics of the session include but are not limited to, the following:

1. Distributed security control and privacy protection in networked systems
2. Analysis and monitoring of networking critical infrastructures
3. Distributed control of complex cyber-physical networks
5. Distributed optimization of networked systems
6. Distributed fault-tolerant controller design for complex cyber-physical networks
7. Analysis and control of transport network
8. Collective behaviors in large-scale networks

4. Invited Session Title: Advances in Analysis and Control of Multi-agent Systems 

Invited Session Chairs:

Chair: Junjie Fu, Southeast University; fujunjie89@gmail.com

Co-chairs: Jingyao Wang, Xiamen University; yayale.8@163.com

 

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Due to the rapid development of computing, communication and sensing technology, these kinds of engineering systems become more and more ubiquitous in real life. Their applications can be found in areas such as mobile sensor networks, autonomous vehicle formations, intelligent transportation systems, smart grids and so on. The large scale of the multi-agent system generally prevents the adoption of a centralized control strategy. As an alternative control approach, distributed control is more appealing as it only requires each agent interacting with limited number of neighboring agents. The main difficulties will be achieving global coordination tasks in the face of various agent dynamics constraints and communication limitations.

This invited session will focus on new analysis and synthesis approaches for multi-agent systems. Research on various aspects of multi-agent systems control such as modeling, controller design, new coordination tasks and real world applications will be welcomed. It aims to provide an international forum for researchers in various fields such as applied mathematics, social science, control engineering, as well as computer science to present, share, and summarize the most recent developments and ideas on related topics. Topics of the session include but are not limited to, the following:

1. Modeling, identification, and optimization of multi-agent systems
2. Consensus, flocking, and containment control of multi-agent systems
3. Robust distributed control methods
5. Distributed control subject to input saturation
6. Sampled-data and event-triggered distributed control
7. Distributed diagnosis and fault-tolerant control
8. Distributed control of autonomous vehicle networks

5. Invited Session Title: Computational Network Science in Big Data Era

Invited Session Chairs:

Chair: Qi Xuan, Zhejiang University of Technology; xuanqi@zjut.edu.cn

Co-chairs: Yongxiang Xia, Zhejiang University; xiayx@zju.edu.cn; Jinyin Chen, Zhejiang University of Technology; chenjinyin@zjut.edu.cn;

 

Nowadays, more and more data are collected to better understand many complex systems, such as social and biological systems. For example, many online communities, such as open-source software projects, electronic commerce, Q&A website, Wikipedia etc, record various electronic traces of the different kinds of human activities which are an empirical goldmine that can enable the holistic study and understanding of these social systems. In biology, high-dimensional gene data can be used to establish gene networks, which may help to discover cancer related genes and further design new targeting drugs.

This invited session will focus on using network-based, sequence analysis, machine learning methods to analyze the various data sets collected from social and biological systems, discover knowledge, establish models, providing theory, and further predict future behaviors. Topics of the session include but are not limited to, the following:

1. New methods to analyze and model real social and biological network
2. Statistical models for the relationship between the structural properties and the efficiency for complex systems
3. New methods to find essential nodes and links in networks
4. Network-based recommendation systems
5. Using network and machine learning methods to predict online behaviors
6. Machine learning methods in network science
7. Network alignment and node matching between networks
8. Temporal and layered network models based on real data