  Event-based state estimation can achieve estimation quality comparable to
traditional time-triggered methods, but with a significantly lower number of
samples. In networked estimation problems, this reduction in sampling instants
does, however, not necessarily translate into better usage of the shared
communication resource. Because typical event-based approaches decide
instantaneously whether communication is needed or not, free slots cannot be
reallocated immediately, and hence remain unused. In this paper, novel
predictive and self triggering protocols are proposed, which give the
communication system time to adapt and reallocate freed resources. From a
unified Bayesian decision framework, two schemes are developed: self-triggers
that predict, at the current triggering instant, the next one; and predictive
triggers that indicate, at every time step, whether communication will be
needed at a given prediction horizon. The effectiveness of the proposed
triggers in trading off estimation quality for communication reduction is
compared in numerical simulations.
