Targeting uncertainty in smart CPS by confidence-based logic

Tomáš Bureš, Petr Hnětynka, František Plášil*, Dominik Škoda, Jan Kofroň, Rima Al Ali, Ilias Gerostathopoulos

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Since Smart Cyber–Physical Systems (sCPS) are complex and decentralized systems of dynamically cooperating components, architecture-based adaptation is of high importance in their design. In this context, a key challenge is that they typically operate in uncertain environments. Thus, an inherent requirement in sCPS design is the need to deal with the uncertainty of data coming from the environment. Existing approaches often rely on the fact that an adequate model of the environment and/or base probabilities or a prior distribution of data are available. In this paper, we present a specific logic (CB logic), which, based on statistical testing, allows specifying transition guards in architecture-based adaptation without requiring knowledge of the base probabilities or prior knowledge about the data distribution. Applicable in state machines’ transition guards in general, CB logic provides a number of operators over time series that simplify the filtering, resampling, and statistics-backed comparisons of time series, making the application of multiple statistical procedures easy for non-experts. The viability of our approach is illustrated on a running example and a case study demonstrating how CB logic simplifies adaptation triggers. Moreover, a library with a Java and C ++ implementation of CB logic's key operators is available on GitHub.

Original languageEnglish
Article number111065
Pages (from-to)1-15
Number of pages15
JournalJournal of Systems and Software
Early online date12 Aug 2021
Publication statusPublished - Nov 2021

Bibliographical note

Funding Information:
The work has been partially supported by the project no. LTE117003 (ESTABLISH) from the INTER-EUREKA LTE117 programme by the Ministry of Education, Youth, and Sports of the Czech Republic and partially supported by the Charles University institutional funding SVV 260451 .

Publisher Copyright:
© 2021 Elsevier Inc.


  • Adaptation
  • Smart cyber–physical systems
  • Software architecture
  • Statistical testing
  • Uncertainty


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