Self-adaptive Cyber-physical Systems Research Articles

RA4Self-CPS: A Reference Architecture for Self-adaptive Cyber-Physical Systems

RA4Self-CPS: A Reference Architecture for Self-adaptive Cyber-Physical Systems

Defining adaptivity and logical architecture for engineering (smart) self-adaptive cyber–physical systems

Modern cyber–physical systems (CPSs) are embedded in the physical world and intrinsically operate in a continuously changing and uncertain environment or operational context. To meet their business goals and preserve or even improve specific adaptation goals, besides the variety of run-time uncertainties and changes to which the CPSs are exposed—the systems need to self-adapt.

State-Constraint Transition: A Language for the Formal Specification of Dynamic Cyber-System Requirements

Existing formal languages for the specification of self-adaptive cyber-physical systems focus on re-configuring the system-to-be depending on its current context, to satisfy the user’s requirements, that is by dynamically composing the software’s structure and behavior. While these approaches specify context-sensitive requirements, they rarely consider their run-time dynamic and scalable nature. The State-Constraint Transition (SCT) modeling language, introduced in this paper, provides an answer to the problems linked to the specification of dynamic requirements by introducing the concept of configuration states, in which requirements are translated into constraints. The expressiveness of existing approaches is thus extended, combining the ease of use of well-established notations, notably those based on characteristics, and those based on Finite-state Machines (FSM), with the computational power and expressiveness of the constraint programming approach. The paper briefly presents the results of the preliminary evaluation, which assesses the expressiveness, scalability, and domain independence of the SCT language.

State-Constraint Transition: A Language for the Formal Specification of Dynamic Cyber-System Requirements

Existing formal languages for the specification of self-adaptive cyber-physical systems focus on re-configuring the system-to-be depending on its current context, to satisfy the user’s requirements, that is by dynamically composing the software’s structure and behavior. While these approaches specify context-sensitive requirements, they rarely consider their run-time dynamic and scalable nature. The State-Constraint Transition (SCT) modeling language, introduced in this paper, provides an answer to the problems linked to the specification of dynamic requirements by introducing the concept of configuration states, in which requirements are translated into constraints. The expressiveness of existing approaches is thus extended, combining the ease of use of well-established notations, notably those based on characteristics, and those based on Finite-state Machines (FSM), with the computational power and expressiveness of the constraint programming approach. The paper briefly presents the results of the preliminary evaluation, which assesses the expressiveness, scalability, and domain independence of the SCT language.

A sustainable-development approach for self-adaptive cyber–physical system’s life cycle: A systematic mapping study

Cyber–Physical Systems (CPS) refer to a new generation of systems where the cyber and physical layers are –strongly– interconnected. The development of these systems requires two fundamental parts. First, the design of sustainable architectures –centered on adaptation, throughout a System-Development Life-Cycle (SDLC)– to develop robust and economically profitable products. Second, the use of self-adaptive techniques to adjust CPSs to the evolving circumstances of their operation context. This work presents a systematic mapping study (SMS) that discusses different approaches used to develop self-adaptive CPSs (SA-CPSs) at each stage of the SDLC, focused on sustainability. The results show trends such as (i) Designs are not limited to particular application domains, (ii) Performance was the most commonly used attribute, and (iii) Monitor–Analyze–Plan–Execute over a shared Knowledge (MAPE-K) is the predominant feedback loop applied in the cyber layer. The results also raise challenges such as (i) How to design and evaluate sustainable SA-CPSs, (ii) How to apply unit and integration testing in the development of SA-CPSs, and (iii) How to develop feedback loops on SA-CPSs with the integration of machine-learning techniques.