  We consider the problem of distributed Kalman filtering for sensor networks
in the case there are constraints in data transmission and there is model
uncertainty. More precisely, we propose two distributed filtering strategies
with event-triggered communication where the state estimators are computed
according to the least favorable model. The latter belongs to a ball about the
nominal model. We also show that both the methods are stable in the sense that
the mean-square of the state estimation error is bounded in all the nodes.
