SmartData: Toward the Data-Driven Design of Critical Systems
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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10912475/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850034170477150208 |
|---|---|
| author | Jose L. Conradi Hoffmann Antonio A. Frohlich |
| author_facet | Jose L. Conradi Hoffmann Antonio A. Frohlich |
| author_sort | Jose L. Conradi Hoffmann |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9f77482536e94ac0a9f34699a8919cf8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9f77482536e94ac0a9f34699a8919cf82025-08-20T02:57:55ZengIEEEIEEE Access2169-35362025-01-0113418654188610.1109/ACCESS.2025.354854210912475SmartData: Toward the Data-Driven Design of Critical SystemsJose L. Conradi Hoffmann0https://orcid.org/0000-0002-3108-7650Antonio A. Frohlich1https://orcid.org/0000-0002-4063-1339Software/Hardware Integration Laboratory, Federal University of Santa Catarina, Florianópolis, Santa Catarina, BrazilSoftware/Hardware Integration Laboratory, Federal University of Santa Catarina, Florianópolis, Santa Catarina, BrazilMachine 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.https://ieeexplore.ieee.org/document/10912475/Data-drivencritical systems designcyber-physical systemsdata timingSmartData |
| spellingShingle | Jose L. Conradi Hoffmann Antonio A. Frohlich SmartData: Toward the Data-Driven Design of Critical Systems IEEE Access Data-driven critical systems design cyber-physical systems data timing SmartData |
| title | SmartData: Toward the Data-Driven Design of Critical Systems |
| title_full | SmartData: Toward the Data-Driven Design of Critical Systems |
| title_fullStr | SmartData: Toward the Data-Driven Design of Critical Systems |
| title_full_unstemmed | SmartData: Toward the Data-Driven Design of Critical Systems |
| title_short | SmartData: Toward the Data-Driven Design of Critical Systems |
| title_sort | smartdata toward the data driven design of critical systems |
| topic | Data-driven critical systems design cyber-physical systems data timing SmartData |
| url | https://ieeexplore.ieee.org/document/10912475/ |
| work_keys_str_mv | AT joselconradihoffmann smartdatatowardthedatadrivendesignofcriticalsystems AT antonioafrohlich smartdatatowardthedatadrivendesignofcriticalsystems |