Model-Based Engineering of Runtime Reconfigurable Networked Embedded Systems

  • Coen van Leeuwen
  • Yolanda Rieter-BarrellEmail author
  • Zoltan Papp
  • Andrei Pruteanu
  • Teus Vogel
Part of the Internet of Things book series (ITTCC)


Today’s societal challenges, such as sustainable urban living and public safety and security require monitoring and control solutions for large-scale complex and dynamical systems. The distinguishing features of these systems are serious resource constraints, demanding non-functional requirements such as robustness, timeliness, lifetime and the capability of handling system evolution through runtime reconfiguration. In this chapter, a multi-aspect modeling language is introduced that allows system designers to model the architecture of large scale networked systems from different aspects. This modeling language introduces innovative concepts to model runtime reconfiguration at design-time. The proposed architecture for modeling runtime reconfiguration consists of primary tasks in one layer and secondary management tasks in another layer. Special reconfiguration primitives allow the description of four types of reconfiguration: re-parameterisation, re-instantiation, rewiring and relocation. The modeling language is accompanied by a modeling and design methodology (inspired by the MAPE-K technique [1]) and uses feedback loops in the system model to realize runtime reconfiguration. This chapter also proposes Key Performance Indicators (KPIs) that allow designers to quantify the “quality” of the system designs and pick the most promising one. Special attention is paid to the fact that the availability of a runtime reconfiguration (i.e. re-design capability) in a system requires KPIs to be derived and evaluated at runtime as a precondition for guiding the reconfiguration process.


Modeling Language Task Group Hardware Resource Task Graph Execution Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Coen van Leeuwen
    • 1
  • Yolanda Rieter-Barrell
    • 1
    Email author
  • Zoltan Papp
    • 1
  • Andrei Pruteanu
    • 2
  • Teus Vogel
    • 1
  1. 1.TNOThe HagueThe Netherlands
  2. 2.TU DelftDelftThe Netherlands

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