Dr. Jing Wang, Professor

Department of Electrical Engineering, Illinois State University, Normal, USA

Biography

Dr. Jing Wang received the Ph.D. degree in control theory and applications from the Central South University, Changsha, China, in 1997. He is a Professor of Electrical Engineering at Illinois State University. Prior to this, he served in the Department of Electrical and Computer Engineering at Bradley University, Peoria, IL, where he was an Assistant Professor from 2014 to 2018, an Associate Professor from 2018 to 2024, and a Professor in 2024. From 2007 to 2014, he held positions as an Assistant Professor and Associate Professor in the Department of Computer Engineering and Computer Science at Bethune-Cookman University, Daytona Beach, FL. Between 2002 and 2007, he was a Research Assistant Professor at the School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL. Earlier in his career, he worked as a Research Fellow at the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (1997-1999), and at the Department of Electrical and Computer Engineering, National University of Singapore, Singapore (1999-2002). He has authored a book published by Springer and over 150 papers, along with four book chapters, primarily focused on nonlinear control, cooperative control, adaptive neural control, robotics, and optimization. His current research interests encompass systems and controls, the Internet of Things (IoT), cyber-physical systems, distributed control, data-driven control and machine learning, distributed optimization, and control applications in robotics, power network, and communication networks. He is a Senior Member of the IEEE.

Topic

Distributed Data-Driven Control, Estimation and Optimization of Multiagent Systems

Abstract

Multiagent systems may be generically defined as a group of dynamical systems in which certain emergent behaviors are exhibited through the local interaction among group members that individually have the capability of self-operating. Multiagent systems are often observed in nature, such as fish schooling, bird flocking, biochemical reaction network, and synchronization of fireflies. Motivated by the e?ciency, flexibility and robustness exhibited by natural multiagent systems, engineered multiagent systems have been developed and studied as seen in robotic networks, power grids, computer networks, and sensor networks. In particular, multiagent systems have found a wide range of potential applications in surveillance and reconnaissance, cooperative exploration for search and rescue missions, environmental sensing and monitoring, cooperative transportation, and congestion and flow control of networks. In this talk, we present design methods and stability analysis for distributed coordination control of multiagent systems using data-driven and learning approaches. The developments are for general dynamical systems with complex system dynamics and time-varying sensing/communication topologies. The connectivity conditions on sensing/communication among agents are identified for achieving the desired coordination. Cooperative control protocols are designed for both linear agents and nonlinear agents. The results on optimal cooperative control design will also be presented and discussed. Simulation and experimental results on applications to multiple robot control will be provided to illustrate the e?ectiveness of the proposed cooperative controls.

Dr. Chao Fang, Associate Professor

School of Information Science and Technology, Beijing University of Technology, Beijing, China

Biography

Dr. Chao Fang received his B.S degree in Information Engineering from Wuhan University of Technology, Wuhan, China, in 2009, and the Ph.D. degree with the State Key Laboratory of Networking and Switching Technology in Information and Communication Engineering from Beijing University of Posts and Te4lecommunications, Beijing, China, in 2015. He joined the Beijing University of Technology in 2016 and now is an associate professor. From August 2013 to August 2014, he had been funded by China Scholarship Council to visit Carleton University, Ottawa, ON, Canada, as a joint doctorate. Moreover, he is the visiting scholars of University of Technology Sydney, Commonwealth Scientific and Industrial Research Organization, Hong Kong Polytechnic University, Kyoto University, Muroran Institute of Technology, and Queen Mary University of London. Dr. Fang is the senior member of IEEE, and the vice chair of technical affairs committee in IEEE ComSoc Asia/Pacific Region (2022-2023). Moreover, he served as the Technical Program Committee Chair of SPCNC 2024, the Session Chairs of ICC 2015, ICCC 2023, and WCNC 2024, Workshop Chairs of ICFEICT (2022-2024) and ICNCIC (2023-2024), and Poster Co-Chair of HotICN 2018. He won the Best Paper Award of IEEE ICFEICT 2022 and 2024, ICCSN 2024, and NCIC 2024. His current research interests include future networks, cloud-edge-terminal cooperation computing, and intelligent network control.

Topic

Collaborative Allocation and Intelligent Optimization of Service-Driven Cloud Radio Access Network Resources

Abstract

In order to meet the service requirements of the emerging applications such as extended reality, 8K ultra-high definition video transmission and industrial Internet of Things in terms of massive user access, heterogeneous mobile traffic processing, ultra-low latency, ultra-high reliability and other aspects, cloud-edge collaboration, as the core of cloud radio access networks (C-RAN), has been increasingly concerned and risen to the height of national development strategy. At present, the problem on cooperative allocation and optimization of cloud-edge-end resources in C-RAN is still in the initial research stage, lacking systematic and in-depth research, which makes it difficult to adaptively guarantee the differentiated service requirements of network business. Therefore, by sorting out and referring to the research ideas and methods related to cloud computing and fog computing, and drawing on future network concepts such as "separation of control and forwarding" in software-defined networking and "in-network caching" in information-centric networking, the project focuses on collaborative allocation and intelligent optimization mechanisms of service-driven C-RAN resources from the perspective of cross-layer and cross-domain cooperation. To improve the overall service capacity and satisfy the differentiated service requirements of massive applications, key technologies such as multi-user-oriented cross-layer collaboration allocation and intelligent optimization of cloud-edge-terminal resources, multi-business-oriented cross-layer collaboration and intelligent resource allocation, multi-business-oriented cross-domain collaboration and intelligent resource allocation will be solved in C-RAN environments, providing customized service for network applications.