  This paper considers the problem of sensory data scheduling of multiple
processes. There are $n$ independent linear time-invariant processes and a
remote estimator monitoring all the processes. Each process is measured by a
sensor, which sends its local state estimate to the remote estimator. The sizes
of the packets are different due to different dimensions of each process, and
thus it may take different lengths of time steps for the sensors to send their
data. Because of bandwidth limitation, only a portion of all the sensors are
allowed to transmit. Our goal is to minimize the average of estimation error
covariance of the whole system at the remote estimator. The problem is
formulated as a Markov decision process (MDP) with average cost over an
infinite time horizon. We prove the existence of a deterministic and stationary
policy for the problem. We also find that the optimal policy has a consistent
behavior and threshold type structure. A numerical example is provided to
illustrate our main results.
