Systems Science & Control Engineering

Data-Driven Intelligent Networked Systems

The rapid expansion of networked computing systems, fuelled by the Internet of Things (IoT), edge/cloud computing, and next-generation wireless networks, presents significant challenges in managing complexity, heterogeneity, and dynamism. Systems science offers a powerful framework for modelling, analysing, and optimising these distributed systems to achieve scalability, resilience, and intelligent operation. By integrating data-centric and learning-driven approaches, systems science enables holistic solutions for real-time adaptation, resource optimisation, and autonomous coordination in networked environments.

Despite progress, applying systems science to network computing remains a critical research frontier. Key challenges include developing scalable system models, analysing dynamic interactions with limited or noisy data, ensuring robustness in uncertain conditions, and designing adaptive architectures for distributed environments. This special issue seeks novel research contributions that advance systems science for intelligent networked systems. We invite original, high-impact research exploring the synergy of systems science and data-driven methodologies to enhance the performance, security, and efficiency of modern networked complex systems.

Topics of Interest

We invite original, high-impact submissions on topics including, but not limited to:

• Systems modelling and analysis for intelligent network computing architectures
• Data-driven system optimisation for traffic prediction, resource allocation, and load balancing
• Distributed system architectures for autonomous and cooperative networked environments
• Scalable system frameworks for edge/cloud computing under uncertainty
• Machine learning-enhanced system models for real-time adaptation in dynamic networks
• Trustworthy and interpretable system designs for transparent network management
• Multi-agent system analysis for decentralised and collaborative network operations
• Digital twins and system simulations for network computing design and evaluation
• Online learning for adaptive system architectures in distributed computing
• Cyber-physical system modelling for next-generation network computing

­­All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is 5/31/2026.

Please contact Commissioning Editor Zhan Yu at zhan.yu@taylorandfrancis.com with any queries and discount codes regarding this Article Collection.

Please be sure to select the appropriate Article Collection from the drop-down menu in the submission system.

Dr. John Panneerselvam focuses on strategically addressing the energy issues of datacentres via scheduling, task allocation, optimising resource provisioning etc. largely driven by descriptive and predictive analytics. John also works in interdisciplinary Artificial Intelligence and digital twin research in space, healthcare, e-commerce etc.

Dr. Liu Yao, a postdoctoral fellow and senior research associate at Lancaster University, and a member of ACM/IEEE/CCF, joined Anhui University in 2023 through the introduction of overseas high-level talents (at the associate professor level). His main research areas include artificial intelligence, sustainable computing, and time series prediction and analysis. He has participated in several research projects funded by the UK's Engineering and Physical Sciences Research Council (EPSRC) and the European Union's Horizon projects. Currently, he has published more than 20 papers in authoritative journals and conferences both domestically and internationally.

Dr. Zhijun Ding (Senior Member, IEEE) received the PhD degree from Tongji University, Shanghai, China, in 2007. Currently he is a professor with the Department of Computer Science and Technology, Tongji University, Shanghai, China. His research interests include formal method, services computing, and Workflow. He has published more than 100 papers in domestic and international academic journals and conference proceedings.