  In networked systems, state estimation is hampered by communication limits.
Past approaches, which consider scheduling sensors through deterministic
event-triggers, reduce communication and maintain estimation quality. However,
these approaches destroy the Gaussian property of the state, making it
computationally intractable to obtain an exact minimum mean squared error
estimate. We propose a stochastic event-triggered sensor schedule for state
estimation which preserves the Gaussianity of the system, extending previous
results from the single-sensor to the multi-sensor case.
