Abstract
Uncertainty is intrinsic in most technical systems, including Cyber-Physical Systems (CPS). Therefore, handling uncertainty in a graceful manner during the real operation of CPS is critical. Since designing, developing, and testing modern and highly sophisticated CPS is an expanding field, a step towards dealing with uncertainty is to identify, define, and classify uncertainties at various levels of CPS. This will help develop a systematic and comprehensive understanding of uncertainty. To that end, we propose a conceptual model for uncertainty specifically designed for CPS. Since the study of uncertainty in CPS development and testing is still irrelatively unexplored, this conceptual model was derived in a large part by reviewing existing work on uncertainty in other fields, including philosophy, physics, statistics, and healthcare. The conceptual model is mapped to the three logical levels of CPS: Application, Infrastructure, and Integration. It is captured using UML class diagrams, including relevant OCL constraints. To validate the conceptual model, we identified, classified, and specified uncertainties in two distinct industrial case studies.
This work is funded by the U-Test H2020 Project (www.u-test.eu).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Use cases containing scenarios having uncertainty.
- 2.
Such a strictly binary categorization may not be always realistic, since Beliefs could be characterized by degrees of validity. However, in this model, we choose to ignore such subtleties. Specifically, a BeliefStatement is deemed to be valid if it is a sufficient approximation of the truth for the purpose on hand.
- 3.
In this case, the Beliefs would be reflected in the rules that are programmed into the system.
- 4.
However, more information does not necessarily imply a decrease in uncertainty.
- 5.
E.g, many people in the past were absolutely certain that the Earth was flat.
- 6.
“Phenomena” here is intended to cover aspects of objective reality, whereas “notion” covers abstract concepts, such those encountered in mathematics or philosophy.
- 7.
We exclude here from this definition “virtual” BeliefAgents, such as those that might occur in virtual reality systems and computer games.
- 8.
Care should be taken to distinguish between indeterminacy and non-determinism. The latter is only one possible source of indeterminacy.
References
Ali, S., Yue, T.: U-test: evolving, modelling and testing realistic uncertain behaviours of cyber-physical systems. In: Software Testing, Verification and Validation (ICST), pp. 1–2. IEEE (2015)
Broy, M.: Engineering cyber-physical systems: challenges and foundations. In: Proceedings of Complex Systems Design & Management, CSD&M, pp. 1–13 (2013)
Huang, H.-M., Tidwell, T., Gill, C., Lu, C., Gao, X., Dyke, S.: Cyber-physical systems for real-time hybrid structural testing: a case study. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, pp. 69–78 (2010)
Tidwell, T., Gao, X., Huang, H.-M., Lu, C., Dyke, S., Gil, C.: Towards configurable real-time hybrid structural testing: a cyber physical systems approach. In: Proceedings of Object/Component/Service-Oriented Real-Time Distributed Computing, pp. 37–44 (2009)
Tannert, C., Elvers, H.D., Jandrig, B.: The ethics of uncertainty. EMBO reports 8 (2007)
Mishel, M.H.: Uncertainty in illness. Image: J. Nurs. Scholarsh. 20, 225–232 (1988)
Babrow, A.S., Kasch, C.R., Ford, L.A.: The many meanings of uncertainty in illness: toward a systematic accounting. Health Commun. 10, 1–23 (1998)
Han, P.K., Klein, W.M., Arora, N.K.: Varieties of uncertainty in health care a conceptual taxonomy. Med. Decis. Making 31, 828–838 (2011)
Cisco: Cisco Preferred Architecture for Video - Design Overview (2015)
Zhang, M., Selic, B., Ali, S., Yue, T., Okariz, O., Norgren, R.: Understanding uncertainty in cyber-physical systems: a conceptual model. Simula Laboratory Research (2016)
Bammer, G., Smithson, M.: Uncertainty and Risk: Multidisciplinary Perspectives. Routledge, New York (2012)
Lindley, D.V.: Understanding uncertainty (revised edn.). Wiley, Hoboken (2014)
Potter, K., Rosen, P., Johnson, C.R.: From quantification to visualization: a taxonomy of uncertainty visualization approaches. In: Dienstfrey, A.M., Boisvert, R.F. (eds.) Uncertainty Quantification in Scientific Computing. IFIP AICT, vol. 377, pp. 226–249. Springer, Heidelberg (2012)
Taylor, B.N.: Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results (rev. DIANE Publishing 2009)
Wasserkrug, S., Gal, A., Etzion, O.: A taxonomy and representation of sources of uncertainty in active systems. In: Etzion, O., Kuflik, T., Motro, A. (eds.) NGITS 2006. LNCS, vol. 4032, pp. 174–185. Springer, Heidelberg (2006)
Cimatti, A., Micheli, A., Roveri, M.: Timelines with Temporal Uncertainty. In: AAAI (2013)
Sprunt, B., Sha, L., Lehoczky, J.: Scheduling sporadic and aperiodic events in a hard real-time system. DTIC Document (1989)
ISO: ISO 31000: Risk management (2009)
Garvey, P.R., Lansdowne, Z.F.: Risk matrix: an approach for identifying, assessing, and ranking program risks. Air Force J. Logistics 22, 18–21 (1998)
Klir, G.: Facets of Systems Science. Springer Science & Business Media, New York (2013)
Yue, T., Briand, L.C., Labiche, Y.: Facilitating the transition from use case models to analysis models: approach and experiments. ACM Trans. Softw. Eng. Methodol. (TOSEM) 22, Article No. 5, 1–38 (2013)
Rajkumar, R.R., Lee, I., Sha, L., Stankovic, J.: Cyber-physical systems: the next computing revolution. In: Proceedings of the 47th Design Automation Conference. ACM (2010)
Conti, M., Das, S.K., Bisdikian, C., Kumar, M., Ni, L.M., Passarella, A., Roussos, G., Tröster, G., Tsudik, G., Zambonelli, F.: Looking ahead in pervasive computing: Challenges and opportunities in the era of cyber–physical convergence. Pervasive Mob. Comput. 8, 2–21 (2012)
Garlan, D.: Software engineering in an uncertain world. In: Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, pp. 125–128. ACM (2010)
Hu, F.: Cyber-Physical Systems: Integrated Computing and Engineering Design. CRC Press, Boca Raton (2013)
Cheng, B.H., Sawyer, P., Bencomo, N., Whittle, J.: A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty. In: Schürr, A., Selic, B. (eds.) MODELS 2009. LNCS, vol. 5795, pp. 468–483. Springer, Heidelberg (2009)
Wan, K., Man, K.L., Hughes, D.: Specification, analyzing challenges and approaches for cyber-physical systems (CPS). Eng. Lett. 18, 308 (2010)
Kerwin, A.: None too solid medical ignorance. Sci. Commun. 15, 166–185 (1993)
Smithson, M.: Ignorance and Uncertainty: Emerging Paradigms. Springer, New York (1989)
Der Kiureghian, A., Ditlevsen, O.: Aleatory or epistemic? Does it matter? Struct. Saf. 31, 105–112 (2009)
Esfahani, N., Malek, S.: Uncertainty in self-adaptive software systems. In: De Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Self-Adaptive Systems. LNCS, vol. 7475, pp 214–238. Springer, Heidelberg (2013)
Ziv, H., Richardson, D., Klösch, R.: The uncertainty principle in software engineering. In: Proceedings of the 19th International Conference on Software Engineering (1997)
Matthies, H.G.: Quantifying uncertainty: modern computational representation of probability and applications. In: Extreme Man-Made and Natural Hazards in Dynamics of Structures, pp. 105–135. Springer, The Netherlands (2007)
Bell, S.: A Beginner’s Guide to Uncertainty of Measurement. National Physical Laboratory Teddington, Middlesex (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, M., Selic, B., Ali, S., Yue, T., Okariz, O., Norgren, R. (2016). Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model. In: Wąsowski, A., Lönn, H. (eds) Modelling Foundations and Applications. ECMFA 2016. Lecture Notes in Computer Science(), vol 9764. Springer, Cham. https://doi.org/10.1007/978-3-319-42061-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-42061-5_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42060-8
Online ISBN: 978-3-319-42061-5
eBook Packages: Computer ScienceComputer Science (R0)