  We consider the minimization of the cost of actuation error under resource
constraints for real-time tracking in wireless autonomous systems. A
transmitter monitors the state of a discrete random process and sends updates
to the receiver over an unreliable wireless channel. The receiver takes actions
according to the estimated state of the source. For each discrepancy between
the real state of the source and the estimated one, we consider a different
cost of actuation error. This models the case where some states, and
consequently the corresponding actions to be taken, are more important than
others. We provide two algorithms: one reaching an optimal solution but of high
complexity, and one providing a suboptimal solution but with low complexity.
The performance of the two algorithms are quite close as shown by the
simulations.
