  We abstract the core logical functions from applications that require
ultra-low-latency wireless communications to provide a novel definition for
reliability. Real-time applications -- such as intelligent transportation,
remote surgery, and industrial automation -- involve a significant element of
control and decision making. Such systems involve three logical components:
observers (e.g. sensors) measuring the state of an environment or dynamical
system, a centralized executive (e.g. controller) deciding on the state, and
agents (e.g. actuators) that implement the executive's decisions. The executive
harvests the observers' measurements and decides on the short-term trajectory
of the system by instructing its agents to take appropriate actions. All
observation packets (typically uplink) and action packets (typically downlink)
must be delivered by hard deadlines to ensure the proper functioning of the
controlled system. In-full on-time delivery cannot be guaranteed in wireless
systems due to inherent uncertainties in the channel such as fading and
unpredictable interference; accordingly, the executive will have to drop some
packets. We develop a novel framework to formulate the observer selection
problem (OSP) through which the executive schedules a sequence of observations
that maximize its knowledge about the current state of the system. To solve
this problem efficiently yet optimally, we devise a branch-and-bound algorithm
that systematically prunes the search space. Our work is different from
existing work on real-time communications in that communication reliability is
not conveyed by packet loss or error rate, but rather by the extent of the
executive's knowledge about the state of the system it controls.
