Communication scheduling methods for estimation over networks

This work focuses on data communication scheduling methods for estimation over networks. A communication scheduler is a mechanism that determines when a data packet should be sent to the network, given the information pattern available. For stochastic communication schedulers, decisions to send data are based on outcomes of random processes. Two issues are addressed in the dissertation: communication scheduler analysis and communication scheduler optimization.; We propose various types of stochastic communication schedulers based on counting processes. We model the dynamics as jump-diffusion processes and use stochastic analysis methods based on infinitesimal generators. The networks we consider include erasure networks and delayed networks.; We construct optimal stochastic communication schedulers that minimize network usage and maximize estimation performance. We formulate a long-term average cost problem with an unbounded per-stage cost function on a Borel state space, which is solved by dynamic programming techniques. The results shed light on how to schedule data communication for networked control systems (NCSs).; Another contribution is on data rate limitations. We derive conditions for the existence of stable estimators under both the bit rate and the packet rate limitations. The conditions show a trade-off relation between the bit rate and the packet rate. We observe that if the packet size is large, then increasing it (and therefore increasing the bit rate) helps little, and the packet rate becomes the dominant factor. This observation encourages us to think in terms of packets instead of bits, as we do in most parts of the dissertation.