  We consider a system of multiple sources, a single communication channel, and
a single monitoring station. Each source measures a time-varying quantity with
varying levels of accuracy and one of them sends its update to the monitoring
station via the channel. The probability of success of each attempted
communication is a function of the source scheduled for transmitting its
update. Both the probability of correct measurement and the probability of
successful transmission of all the sources are unknown to the scheduler. The
metric of interest is the reward received by the system which depends on the
accuracy of the last update received by the destination and the
Age-of-Information (AoI) of the system. We model our scheduling problem as a
variant of the multi-arm bandit problem with sources as different arms. We
compare the performance of all $4$ standard bandit policies, namely, ETC,
$\epsilon$-greedy, UCB, and TS suitably adjusted to our system model via
simulations. In addition, we provide analytical guarantees of $2$ of these
policies, ETC, and $\epsilon$-greedy. Finally, we characterize the lower bound
on the cumulative regret achievable by any policy.
