CNS-2008463 CNS Core: Small: Dynamic and Composite Resource Management in Large-scale Industrial IoT Systems

PI: Prof. Song Han    University of Connecticut

 

Project Summary

Industrial Internet of Things (IIoT) has been making its way into a wide range of industrial applications in recent years. An IIoT paradigm aims at creating a unified sensing, computing and control framework to interconnect all the industrial assets with information systems and business processes, and to streamline the manufacturing process and lead to optimal industrial operations. Because IIoT applications – including autonomous driving and smart highway, manufacturing automation with robots, etc. – are distinguished from commercial IoT by 1) stringent performance guarantees (timing constraints); 2) certifiable reliability and robustness (ability to tolerate environmental unpredictabilities), research is needed to provide a holistic resource management framework that enables effective sensing and control operations in the presence of intermittent data sources and unpredictable system disturbances.

The objective of this project is to help lay the foundation for such a framework by formulating and investigating three fundamental questions: 1) How to achieve real-time data retrieval with intermittent data sources and large-scale high-speed wireless control with guaranteed reliability and minimized jitters? 2) How to perform dynamic and distributed packet scheduling to compensate for unexpected system disturbances in complex industrial environments? 3) How to perform composite resource management to jointly consider network and computing resources for resource scheduling within one IIoT application, and partitioning and reconfiguration among multiple IIoT applications? By addressing these questions, the designed models, analysis methods and algorithms will be validated using high-fidelity IIoT simulation tools and deployed on university-industry co-established IIoT testbeds for thorough performance evaluation in both laboratory and industrial environments.

 

Project Publications

[TMC] Tianyu Zhang, Tao Gong, Mingsong Lyu, Nan Guan, Song Han, Xiaobo Sharon Hu, “Reliable Dynamic Packet Scheduling Over Lossy Real-Time Wireless Networks”, accepted and to appear in IEEE Transactions on Mobile Computing (TMC).

[DAC’22-Poster] Vincent Chau, Chenchen Fu, Shu Han, Song Han, Minming Li, Peng Wu and Yingchao Zhao. “Joint Resource Scheduling in Wireless Networked Control Systems with Energy Constraint”, accepted and presented in the 59th Design Automation Conference (DAC) Poster sessions.

[RTSS’21] Peng Wu, Chenchen Fu, Minming Li, Yingchao Zhao, Jason Xue, Song Han, “Composite Resource Scheduling in Networked Control Systems”, in Proceedings of the 42nd IEEE Real-Time Systems Symposium (RTSS), pp. 162-175, 2021.

[RTSJ] Wei-Ju Chen, Peng Wu, Pei-Chi Huang, Aloysius K. Mok, Song Han, “Online Reconfiguration of Regularity-based Resource Partitions in Cyber-Physical Systems”, Real-Time Systems Journal 57, 302–345, 2021.

 

Postdoc and Graduate Students

Dr. Tianyu Zhang (Postdoc), Peng Wu (PhD student)

 

Classes related to the project

Spring 2023: CSE5300 Advanced Computer Networks

Fall 2022: SE5402 Architecture of IoT

Spring 2022: SE5402 Architecture of IoT