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A System of System (SoS) stems from the integration of existing systems (legacy systems), normally operated by different organizations, and new systems that have been designed to take advantage of this integration.

In the past twenty years the view on how we engineer, operate and evolve independently owned and managed - in order to realize and optimize complex economical processes has started to change. Advances in telecommunications and automation accompanied by standardization efforts resulted in sophisticated cross-domain information and communication technologies (e.g., the Internet of Things (IoT) [2, 9], elastic processing and storage clouds, Web Services) that allow for the integration of more and more existing and previously technologically isolated CPSs. These became cooperating of evolving Cyber-Physical Systems-of-Systems (CPSoSs) and – by their physical and cyber interaction – give rise to new emergent services that cannot be realized by any single or small number of CSs alone.

The essence of the concept emergence is aptly communicated by the following quote, attributed to Aristotle, who lived more than 2000 years ago:

In the European Union FP7-610535-AMADEOS project, a conceptual model for Systems of Systems (SoSs) has been conceived to find a common language allowing experts to collaborate on modelling, engineering, and analyzing SoSs (see public deliverable D2.3 “AMADEOS conceptual model - Revised” []).

This chapter defines the overall tool-supported “AMADEOS architectural framework”, with its main building blocks and interfaces. It particularly focuses on Structure, Dependability, Security, Emergence, and Multi-criticality viewpoints of an SoS.

Since many years, it has been acknowledged that the role of time is fundamental to the design of distributed algorithms []. This is exacerbated in cyber-physical distributed systems, and consequently in Systems-of-Systems, where it is sometimes impossible to say which one of two observed environmental events occurred first.

SoS dynamicity refers to short-term changes in an SoS, which occur in response to changing environmental or operational parameters of the CSs. These changes may have different effects, such as SoS adaptation or the generation of emergent phenomena. This chapter starts by recalling the MAPE approach in Sect.  before to introduce existing monitoring approaches in Sect. . Finally, Sect.  overviews existing reconfiguration techniques for SoS dynamicity management, related to Analyzis, Planning and Execution phases and illustrates through an examples the possible implementations of dynamicity management with modelling and feedback control techniques.

In this chapter we present three case studies in the smart grid domain: Electrical Vehicle charging, Household Management, and an integrated case study that combines the first two together with ancillary services.