Book chapter

Title:
Patterns for Self-Adaptation in Cyber-Physical Systems
Authors:
Angelika Musil, Juergen Musil, Danny Weyns, Tomáš Bureš, Henry Muccini, Mohammad Sharaf
Publication:
Multi-Disciplinary Engineering for Cyber-Physical Production Systems: Data Models and Software Solutions for Handling Complex Engineering Projects
Year:
2017
ISBN:
978-3-319-56345-9
Link:

Abstract:
Engineering Cyber-Physical Systems (CPS) is challenging, as these systems have to handle uncertainty and change during operation. A typical approach to deal with uncertainty is enhancing the system with self-adaptation capabilities. However, realizing self-adaptation in CPS, and consequently also in Cyber-Physical Production Systems (CPPS) as a member of the CPS family, is particularly challenging due to the specific characteristics of these systems, including the seamless integration of computational and physical components, the inherent heterogeneity and large-scale of such systems, and their open-endedness.In this chapter we survey CPS studies that apply the promising design strategy of combining different self-adaptation mechanisms across the technology stack of the system. Based on the survey results, we derive recurring adaptation patterns that structure and consolidate design knowledge. The patterns offer problem-solution pairs to engineers for the design of future CPS and CPPS with self-adaptation capabilities. Finally, the chapter outlines the potential of collective intelligence systems for CPPS and their engineering based on the survey results.

BibTeX:
@incollection{musil_patterns_2017,
    title = {{Patterns for Self-Adaptation in Cyber-Physical Systems}},
    author = {Musil, Angelika and Musil, Juergen and Weyns, Danny and Bures, Tomas and Muccini, Henry and Sharaf, Mohammad},
    year = {2017},
    booktitle = {{Multi-Disciplinary Engineering for Cyber-Physical Production Systems: Data Models and Software Solutions for Handling Complex Engineering Projects}},
    editor = {Biffl, Stefan and Lüder, Arndt and Gerhard, Detlef},
    publisher = {Springer},
    doi = {10.1007/978-3-319-56345-9_13},
    isbn = {978-3-319-56345-9},
    pages = {331--368},
    url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-56345-9_13},
}