This research explores how foundation models can transform Cyber-Physical Systems and IoT deployments beyond traditional ML approaches.

• Addresses limitations of supervised learning in handling diverse sensor modalities
• Proposes foundation models as scalable alternatives for complex CPS-IoT environments
• Identifies technical challenges in adapting large AI models to resource-constrained devices
• Outlines opportunities for transferable knowledge across different system configurations

For engineers, this represents a paradigm shift from creating isolated, task-specific models to deploying adaptable AI systems that can understand and respond to physical environments more holistically.

Foundation Models for CPS-IoT: Opportunities and Challenges