  Motivated by various distributed control applications, we consider a linear
system with Gaussian noise observed by multiple sensors which transmit
measurements over a dynamic lossy network. We characterize the stationary
optimal sensor scheduling policy for the finite horizon, discounted, and
long-term average cost problems and show that the value iteration algorithm
converges to a solution of the average cost problem. We further show that the
suboptimal policies provided by the rolling horizon truncation of the value
iteration also guarantee stability and provide near-optimal average cost.
Lastly, we provide qualitative characterizations of the multidimensional set of
measurement loss rates for which the system is stabilizable for a static
network, significantly extending earlier results on intermittent observations.
