GRuM – A Flexible Model-Driven Runtime Monitoring Framework and its Application to Automated Aerial and Ground Vehicles

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.