SmartData: Toward the Data-Driven Design of Critical Systems

Abstract

Machine Learning algorithms and safety models are enabling higher levels of autonomy in modern Cyber-Physical Systems (CPS). Ensuring safe autonomous operation requires strict adherence to timing and security constraints, best expressed in terms of the data consumed rather than tasks executed. This paper introduces a Data-Centric design for Data-Driven Systems using SmartData, a data construct enriched with metadata to encapsulate origin, semantics, and relationships. SmartData interact via Interest relationships, inheriting requirements such as freshness, periodicity, and security. We extend SmartData with six novel stereotypes: Sensor, Storage, Transformer, Secure, Persistent, and Actuator. To facilitate system design, we propose a method to algorithmically build a SmartData Graph (SDG), a directed graph representing the relationships between SmartData elements. The SDG construction algorithm dynamically updates demands for timing, security, and persistence, ensuring data production satisfies all data requirements. Therefore, a Data-Driven design that can be built directly from the system's data requirements at early states. With the notion of how actuation is expected, we comprise the dataflows necessary to perform this actuation. This approach allows system designers to estimate latency, bandwidth, and data generation periodicity while identifying critical paths requiring reliable communication and processing technologies. The SmartData API bridges design and implementation, enabling seamless integration. We demonstrate the proposed method through a use case of an imitation-learning-based autonomous driving system implemented on a Linux platform and integrated with the CARLA simulator.