Age- and deviation-of-information of hybrid time- and event-triggered systems: What matters more, determinism or resource conservation?

Age-of-information is a metric that quantifies the freshness of information obtained by sampling a remote sensor. In signal-agnostic sampling, sensor updates are triggered at certain times without being conditioned on the actual sensor signal. Optimal update policies have been researched and it is accepted that periodic updates achieve smaller age-of-information than random updates. We contribute a study of a signal-aware policy, where updates are triggered randomly by a defined sensor event. By definition, this implies random updates and as a consequence inferior age-of-information. Considering a notion of deviation-of-information as a signal-aware metric, our results show, however, that event-triggered systems can perform equally well as time-triggered systems while causing smaller mean network utilization. We use the stochastic network calculus to derive bounds of age- and deviation-of-information that are exceeded at most with a small, defined probability. We include simulation results that confirm the tail decay of the bounds. We also evaluate a hybrid time- and event-triggered policy where the event-triggered system is complemented by a minimal and a maximal update interval.