Age of Information in Industrial Cyber-Physical Systems: A Survey on Co-Design, Optimization, and Future Directions

Abstract

The Age of Information (AoI) has emerged as a critical metric for quantifying information freshness in Industrial Cyber-Physical Systems (ICPS), where timely data is paramount for control performance and safety. While numerous optimization strategies exist, the field lacks a systematic overview structured from a functional co-design perspective. Our work provides a comprehensive survey that synthesizes and categorizes the state-of-the-art in AoI optimization by structuring the field around the core operational functionalities of ICPS: sampling and estimation, scheduling and optimization, as well as control and communication co-design. We first establish the theoretical foundations of AoI and its key variants. Then, for each functional block, we critically review and compare both foundational model-based strategies and advanced learning-based paradigms. A particular emphasis is placed on complex, real-world scenarios, offering a multi-dimensional evaluation of optimization strategies to highlight the trade-offs between different methodologies. Finally, we identify critical unresolved challenges, spanning from the theory-practice gap to the need for lightweight, robust algorithms, and outline a concrete roadmap for future research. This survey serves as a structured guide for researchers and practitioners, aiming to advance the field by bridging theoretical frameworks with the practical demands of next-generation industrial systems.