Keynote Title: Enhancing Data Quality in the Industrial Internet of Things: AI-Driven Solutions for Reliable and Resilient Systems 

Abstract: Data quality in the Industrial Internet of Things (IIoT) is critical for maintaining the reliability and safety of AI-driven, safety-critical cyber-physical systems. In this keynote, we explore recent advancements in machine learning pipelines tailored for addressing the data quality challenges inherent in IIoT environments. These pipelines, developed for edge-to-cloud architectures, enable the continuous validation, repair, and enhancement of data streams from industrial sensors. We present case studies showcasing virtual and uncertainty-aware sensors designed to detect and rectify data issues such as bias, drift, and precision degradation, thereby enhancing system resilience. By integrating blockchain for trusted data sharing, we also address security and traceability in collaborative environments. This keynote will discuss the implications of these innovations for zero-defect manufacturing and sustainable industrial practices in the evolving landscape of Industry 4.0. 

Keynote Speaker Biography: Arda Goknil is a senior research scientist with SINTEF, Norway (https://www.sintef.no/en/all-employees/employee/arda.goknil/). He received his PhD degree in computer science from the University of Twente, the Netherlands, in 2011. Before moving to Norway, he was a research associate with the Interdisciplinary Centre for Security, Reliability, and Trust (SnT) at the University of Luxembourg. His research concerns AI engineering, data quality, model-driven engineering, software testing, software security, intermittent computing, product line engineering, and requirements engineering. He is active on EU-funded and national research projects with several academic and industry partners.