Time-Sensitive Networking for industrial IoT: integration, analysis, and performance evaluation

Industrial automation networks demand precise timing, minimal latency, and negligible packet loss for efficient real-time data exchange. Time-Sensitive Networking (TSN) emerges as a crucial technology for future automation, promis ing enhanced timing accuracy, reduced packet delay, and improved networking determinism. This thesis explores and innovates within TSN functionalities to address key aspects of industrial networking and related technologies. The critical need for reliable real-time data exchange across industries is examined, introducing TSN principles such as time synchronisation, deterministic communication, traffic shaping, and Quality of Service (QoS) assurances. Through simulation, typical industrial use cases and traffic requirements are evaluated, focusing on priority queuing, Time Aware shaping (TAS), and Credit Based Shaping (CBS) to meet latency constraints. The findings demonstrate TSN’s ability to orchestrate network traffic while adhering to strict timing requirements, highlighting its practical relevance in industrial automation. In smart manufacturing environments, the optimisation of industrial networks for Quality Control and Classification After Production (QCAP) is emphasised. By leveraging TSN standards, diverse QoS requirements are addressed to enhance efficiency and reliability. Fault tolerance in Industrial Internet of Things (IIoT) applications is also investigated using network calculus principles to analyse worst-case latency, providing insights into network performance and stability. The research integrates TSN with Software Defined Networks (SDN) to manage network configurations, focusing on traffic scheduling in industrial applications. Network-device contracts are proposed for traffic schedule computation and distribution, demonstrating scalability through Mininet emulation. Additionally, Wi-Fi is explored as a complementary technology for IoT applications, evaluating its potential to reduce latency and enhance industrial automation. This thesis offers a comprehensive analysis of TSN performance across various scenarios and its integration with complementary technologies, providing valuable insights for advancing industrial automation and connectivity within the industry 4.0 paradigm.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2024

Author dc:creator

• Seliem, Mohamed

Advisors dc:contributor.advisor

• Pesch, Dirk H J
• Zahran, Ahmed

• Time-Sensitive Networking
• Software-defined networking
• Network calculus
• Performance evaluation
• Real-time communication

Statement dc:rights

• © 2024, Mohamed Seliem.

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/16388

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/16388