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Online monitoring is a widely used technique in assessing if the performanceof the system satisfies some desired requirements during run-time operation.Existing works on online monitoring usually assume that the monitor can acquiresystem information periodically at each time instant. However, such a periodicmechanism may be unnecessarily energy-consuming as it essentially requires toturn on sensors consistently. In this paper, we proposed a novel self-triggeredmechanism for model-based online monitoring of discrete-time dynamical systemunder specifications described by signal temporal logic (STL) formulae.Specifically, instead of sampling the system state at each time instant, aself-triggered monitor can actively determine when the next system state issampled in addition to its monitoring decision regarding the satisfaction ofthe task. We propose an effective algorithm for synthesizing such aself-triggered monitor that can correctly evaluate a given STL formulaon-the-fly while maximizing the time interval between two observations. We showthat, compared with the standard online monitor with periodic information, theproposed self-triggered monitor can significantly reduce observation burdenwhile ensuring that no information of the STL formula is lost. Case studies areprovided to illustrate the proposed monitoring mechanism.

Sleep When Everything Looks Fine: Self-Triggered Monitoring for Signal Temporal Logic Tasks