title: Wireless communication and control codesign for dynamic QoS strategy in Industrial 4.0
Creators: name: Wang, Shuojie
Publisher: name: University of Glasgow
Year: 2024
Subjects: subject: Wireless control co-design, Dynamic QoS, Industry 4.0, URLLC.

description: Due to its technical characteristics, the 5G network is highly anticipated for its application in the industrial field, particularly in terms of ultra reliability and low latency (URLLC). 5G serves as a pivotal enabling technology that propels the robust development of the industrial Internet, while simultaneously benefiting from it. By leveraging the attributes of 5G URLLC, real-time control over factory automation equipment can be achieved, replacing traditional wired networks and reducing cable and wiring workload significantly. This not only saves valuable adjustment time on production lines but also enhances flexibility within factories. High-performance wireless networks seamlessly connect an array of sensors, robots, and information systems within factories, facilitating data analysis and decision-making processes that are fed back into operations. In the context of Industry 4.0, a multitude of wireless communication resources is required, thus minimizing communication resource consumption while ensuring efficient transmission has become a pressing challenge.

In order to implement dynamic Quality of Service (QoS) strategy in the context of Industry 4.0, this thesis initially provides an overview of the fundamental technologies, applications, and characteristics of 5G networks and Industry 4.0. Subsequently, it considers control and communication co-design problem in wireless network for achieving dynamic QoS strategy in Industry 4.0. In the industrial scenario, it will lead to a certain waste of resources if the task requirements at every moment are in accordance with the requirements of URLLC, while additional resources generated by a dynamic QoS strategy can be provided for other users since the communication and control systems are dynamic. This is especially true for the control system, whose requirements for tasks are changing almost all the time. The objective is thus to achieve maximum system capacity and minimum communication resource consumption. In this research, the proposed method is to use the dynamic QoS strategy based on communication and control co-design system by constructing a use case of a reconfigurable factory architecture for future Industry 4.0. Finally, based on dynamic QoS, three key and challenging research strategies are identified in terms of reducing communication resource consumption and optimizing system capacity.

The first research question and contribution focus on examining the relationship between user customization requirements and communication resource consumption within the context of Industry 4.0. Three mapping schemes are proposed to facilitate the implementation of a co-design control scheme, which encompasses mapping industrial demands to communication bandwidth resource consumption.

The second contribution explores the optimization of communication resource allocation. Implementation of dynamic QoS based on packet length design, the purpose is to minimize the bandwidth consumption of each robot arm. Simulation results show that the proposed solution can significantly reduce wireless resource consumption compared to other benchmarks while ensuring the required control system requirements.

The third contribution examines the impact of channel scheduling on enhancing the performance of dynamic QoS policies. In practical applications, due to varying locations of Automated Guided Vehicles (AGVs), the selection of frequency channels for AGV robot arms differs. The proposed scheme demonstrates that increasing transmit power can effectively increase system capacity.

The fourth contribution achieves the objective of average bandwidth resource consumption through task rescheduling with flexible delay. A task-oriented dynamic resource allocation model is proposed. Simulation results demonstrate that this strategy effectively reduces the peak of resource consumption by deferring low-priority tasks to subsequent idle periods, while maintaining total and average resource consumption unchanged.