This website hosts the Supplementary Online Material
related to our study contributed to the book

"Multi-Disciplinary Engineering for
Cyber-Physical Production Systems"

(Stefan Biffl, Arndt Lüder, Detlef Gerhard, eds.)



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.

Goal of the Study

This study aims to consolidate existing design knowledge on self-adaptation strategies to address uncertainty in CPPS and to identify novel and promising approaches that need further research. Since there is rather a general lack of knowledge on self-adaptation specifically in CPPS, we broaden the scope of our investigation and provide insight on how self-adaptation capabilities of the more general family of CPS are designed. Thus CPPS engineers can learn from application experiences with CPS for dealing with adaptation challenges and concerns in CPPS.
To address this goal, we analyze how state-of-the-art approaches make use of self-adaptation mechanisms and models to handle uncertainty while architecting CPS. In addition, we focus on self-adaptation applied in CPS in the manufacturing domain.
Based on a better understanding of existing developed adaptation strategies to address challenges and concerns of CPS, we seek to closely examine common approaches, considerations and advances to identify recurring patterns, models or tactics. The documentation of such architectural knowledge should support CPPS engineers with the realization and coordination of self-adaptation. In addition, this consolidated design knowledge base can provide a strong foundation for designing self-adaptation capabilities in CPPS engineering that can be further researched and extended by CPPS researchers.

Research Questions

RQ1 - How is self-adaption applied in cyber-physical systems in general?
RQ2 - How can this knowledge be applied and exploited to cyber-physical production systems and their engineering?

Research Method

To answer the research questions we applied an iterative research approach with the following steps. In the first step, we reviewed the state-of-the-art in literature using a systematic mapping study method and consolidated existing design knowledge on self-adaptation strategies in CPS. In the second step, we synthesized and analyzed the collected knowledge to derive recurring adaptation patterns that can be applied for engineering CPPS.

Supplementary Material

The supplementary material on this site intends to provide the interesting reader with a more detailed insight into the planning and results of our study.

  • Study Protocol: Before conducting the study, we defined a study protocol describing the search strategy and selection criteria, data extraction form, data analysis methods, reporting strategy, and threats to validity.

  • Collected Data and Analysis Results: We conducted a systematic mapping study by searching four major scientific data bases, resulting in an initial set of 42 candidate studies from a previously performed SLR and an extended set of 26 studies which are eventually reduced to 13 primary studies for data extraction after applying inclusion and exclusion criteria. After finishing data collection, the results were checked for consistency and completeness as well as documented and analyzed.

  • Table of Synthesized Results: To derive patterns, we carefully studied the collected data and analysis results of the conducted systematic mapping study. We created a large table presenting the concrete designs of self-adaptation mechanisms applied across the technology stack for the application scenario of each investigated study. The comparison across all represented solution designs highlighted areas that follow similar or equal strategies, enabling us to identify adaptation patterns with different combinations of multiples types of self-adaptation within a system.