Section: Overall Objectives

Motivations

Beyond the buzzword, a CPS is an ubiquitous object of our everyday life. CPSs have evolved from individual independent units (e.g an ABS brake) to more and more integrated networks of units, which may be aggregated into larger components or sub-systems. For example, a transportation monitoring network aggregates monitored stations and trains through a large scale distributed system with relatively high latency. Each individual train is being controlled by a train control network, each car in the train has its own real-time bus to control embedded devices. More and more, CPSs are mixing real-time low latency technology with higher latency distributed computing technology.

In the past 15 years, CPS development has moved towards Model Driven Engineering (MDE). With MDE methodology, first all requirements are gathered together with use cases, then a model of the system is built (sometimes several models) that satisfy the requirements. There are several modeling formalisms that have appeared in the past ten years with more or less success. The most successful are the executable models (Matlab/Simulink, https://fr.mathworks.com/products/simulink.html) (Ptolemy, http://ptolemy.eecs.berkeley.edu) (SysML, http://www.uml-sysml.org), i.e., models that can be simulated, exercised, tested and validated. This approach can be used for both software and hardware.

A common feature found in CPSs is the ever presence of concurrency and parallelism in models. Large systems are increasingly mixing both types of concurrency. They are structured hierarchically and comprise multiple synchronous devices connected by buses or networks that communicate asynchronously. This led to the advent of so-called GALS (Globally Asynchronous, Locally Synchronous) models, or PALS (Physically Asynchronous, Logically Synchronous) systems, where reactive synchronous objects are communicating asynchronously. Still, these infrastructures, together with their programming models, share some fundamental concerns: parallelism and concurrency synchronization, determinism and functional correctness, scheduling optimality and calculation time predictability.

Additionally, CPSs monitor and control real-world processes, the dynamics of which are usually governed by physical laws. These laws are expressed by physicists as mathematical equations and formulas. Discrete CPS models cannot ignore these dynamics, but whereas the equations express the continuous behavior usually using real numbers (irrational) variables, the models usually have to work with discrete time and approximate floating point variables.