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This paper considers the problem of sensory data scheduling of multipleprocesses. There are $n$ independent linear time-invariant processes and aremote estimator monitoring all the processes. Each process is measured by asensor, which sends its local state estimate to the remote estimator. The sizesof the packets are different due to different dimensions of each process, andthus it may take different lengths of time steps for the sensors to send theirdata. Because of bandwidth limitation, only a portion of all the sensors areallowed to transmit. Our goal is to minimize the average of estimation errorcovariance of the whole system at the remote estimator. The problem isformulated as a Markov decision process (MDP) with average cost over aninfinite time horizon. We prove the existence of a deterministic and stationarypolicy for the problem. We also find that the optimal policy has a consistentbehavior and threshold type structure. A numerical example is provided toillustrate our main results.

Optimal Scheduling of Multiple Sensors with Packet Length Constraint