Runtime monitoring is critical for ensuring safe operation and for enabling self-adaptive behavior of Cyber-Physical Systems (CPS). Monitors are established by identifying runtime properties of interest, creating probes to instrument the system, and defining constraints to be checked at runtime. For many systems, implementing and setting up a monitoring platform can be tedious and time-consuming, as generic monitoring platforms do not adequately cover domain-specific monitoring requirements. This situation is exacerbated when the System under Monitoring (SuM) evolves, requiring changes in the monitoring platform. Most existing approaches lack support for the automated generation and setup of monitors for diverse technologies and do not provide adequate support for dealing with system evolution. In this paper, we present GRuM (Generating CPS Runtime Monitors), a framework that combines model-driven techniques and runtime monitoring, to automatically generate a customized monitoring platform for a given SuM. Relevant properties are captured in a Domain Model Fragment, and changes to the SuM can be easily accommodated by automatically regenerating the platform code. To demonstrate the feasibility and performance we evaluated GRuM against two different systems using TurtleBot robots and Unmanned Aerial Vehicles. Results show that GRuM facilitates the creation and evolution of a runtime monitoring platform with little effort and that the platform can handle a substantial amount of events and data.

Cyber–Physical Systems (CPS) are increasingly used in safety–critical scenarios where ensuring their correct behavior at runtime becomes a crucial task. Therefore, the behavior of the CPS needs to be monitored at runtime so that violations of requirements can be detected. With the inception of edge devices that facilitate runtime analysis at the edge and the increasingly diverse environments that CPS operate in, flexible monitoring approaches are needed that consider the data that needs to be monitored and the analyses performed on that data. In this paper, we propose AMon, a flexible adaptive monitoring framework that supports the specification and validation of monitoring adaptation rules, using a domain-specific language. Based on these rules, AMon automatically generates code for direct deployment onto devices. We evaluated AMon by applying it to TurtleBot Robots and a fleet of Unmanned Aerial Vehicles. Furthermore, we conducted a user study assessing the understandability and ease of use of our language. Results show that creating multiple adaptation rules with our DSL is feasible with minimal effort, and that adaptive monitoring can reduce the amount of runtime data transmitted from the edge device according to the current state of the system and its monitoring needs.