We are moving towards a massively and diversely connected world populated by a seamless network of intelligent, dynamic distributed systems engaged in a shared interaction with the physical world and each other through unreliable sensors, actuators and noisy communication channels. These systems are extremely delay sensitive, so that coding over long blocks of observed data might not be feasible. Furthermore, information exchanges are geared towards maximizing payoff, rather than towards simply recovering the information sent, as in classical information theory. Finally, causality and feedback are of paramount importance. In this talk, I will show how a combination of information-theoretic and control-theoretic tools can provide important insights into various operational scenarios of remote tracking and control: fully and partially observed rate-constrained control; tracking over rate-limited channels with side information and with multiple observers; jointly optimal sampling and compressing policies. Using the framework of stochastic linear systems, we will compute the fundamental tradeoffs between rate and performance, propose practical coding schemes and point out sensible design practices.

Based on joint works with B. Hassibi and N. Guo.