  We address the problem of how to optimally schedule data packets over an
unreliable channel in order to minimize the estimation error of a
simple-to-implement remote linear estimator using a constant "Kalman'' gain to
track the state of a Gauss Markov process. The remote estimator receives
time-stamped data packets which contain noisy observations of the process.
Additionally, they also contain the information about the "quality'' of the
sensor\ source, i.e., the variance of the observation noise that was used to
generate the packet. In order to minimize the estimation error, the scheduler
needs to use both while prioritizing packet transmissions. It is shown that a
simple index rule that calculates the value of information (VoI) of each
packet, and then schedules the packet with the largest current value of VoI, is
optimal. The VoI of a packet decreases with its age, and increases with the
precision of the source. Thus, we conclude that, for constant filter gains, a
policy which minimizes the age of information does not necessarily maximize the
estimator performance.
