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What are the fundamental software abstractions for designing reliable cyber-physical systems operating in uncertain environments?

Published online by Cambridge University Press:  24 July 2023

David Broman  and
Jim Woodcock
David Broman*
Affiliation:
KTH Royal Institute of Technology, Stockholm, Sweden Stanford University, California, USA
Jim Woodcock
Affiliation:
University of York, York, UK
*
Corresponding author: David Broman; Email: dbro@kth.se
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Extract

Cyber-physical systems (CPS) combine computations in embedded systems with physical dynamics, typically interconnected using networks. Predictability and reliability of combined software and hardware systems are crucial for CPS design (Lee, 2008), but all parts in a real CPS are not necessarily predictable. Specifically, operating in a physical environment introduces different aspects of uncertainty, including uncertainties in sensors, the physical dynamics of the system itself and interactions with other systems or humans. There is a large body of knowledge about reasoning with uncertainty in fields and topics such as statistics, automatic control and sensor fusion. However, how to program for uncertainty in complex cyber-physical systems needs to be better understood. What are the fundamental software abstractions? How do they compose and interact?

Type
Question
Information
Research Directions: Cyber-Physical Systems , Volume 1 , 2023 , e4
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Context

Cyber-physical systems (CPS) combine computations in embedded systems with physical dynamics, typically interconnected using networks. Predictability and reliability of combined software and hardware systems are crucial for CPS design (Lee, Reference Lee2008), but all parts in a real CPS are not necessarily predictable. Specifically, operating in a physical environment introduces different aspects of uncertainty, including uncertainties in sensors, the physical dynamics of the system itself and interactions with other systems or humans. There is a large body of knowledge about reasoning with uncertainty in fields and topics such as statistics, automatic control and sensor fusion. However, how to program for uncertainty in complex cyber-physical systems needs to be better understood. What are the fundamental software abstractions? How do they compose and interact?

Time, timeliness and functional correctness are vital CPS and real-time systems concepts. Many formalisms, languages and tools exist for CPS (Carreira et al., Reference Carreira, Amaral and Vangheluwe2020; Broman et al., Reference Broman, Lee, Tripakis and Törngren2012), including programming and modelling both the cyber part, as well as the physical part. However, there needs to be scientifically proven and practically evaluated approaches that combine software and platform aspects (execution time, scheduling and hardware properties) with methods for handling uncertainties in the physical environment. Probabilistic machine learning (Ghahramani, Reference Ghahramani2015) and probabilistic programming (Goodman, Reference Goodman2013) have emerged as promising directions in a Bayesian setting. However, these approaches have yet to be combined (in a principled way) with the hardware and software aspects of CPS. This research question concerns finding, formalising and evaluating such fundamental software abstractions, including but not limited to programming models, programming language abstractions, operating systems abstractions and modelling abstractions.

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References

Broman, D, Lee, EA, Tripakis, S and Törngren, M (2012) Viewpoints, formalisms, languages, and tools for cyber-physical systems. In Proceedings of the 6th International Workshop on Multi-Paradigm Modeling, pp. 49–54.CrossRefGoogle Scholar
Carreira, P, Amaral, V and Vangheluwe, H (2020) Multi-paradigm modelling for cyber-physical systems: foundations. In Foundations of Multi-Paradigm Modelling for Cyber-Physical Systems. Cham: Springer, pp. 1–14.CrossRefGoogle Scholar
Ghahramani, Z (2015) Probabilistic machine learning and artificial intelligence. Nature 521, 7553, 452–459. https://doi.org/10.1038/nature14541.CrossRefGoogle ScholarPubMed
Goodman, ND (2013) The principles and practice of probabilistic programming. ACM SIGPLAN Notices 48, 1, 399–402. https://doi.org/10.1145/2480359.2429117.CrossRefGoogle Scholar
Lee, EA (2008) Cyber physical systems: design challenges. In 2008 11th IEEE international symposium on object and component-oriented real-time distributed computing (ISORC). IEEE. pp. 363–369.CrossRefGoogle Scholar