  We develop a simple model for the timely monitoring of correlated sources
over a wireless network. Using this model, we study how to optimize
weighted-sum average Age of Information (AoI) in the presence of correlation.
First, we discuss how to find optimal stationary randomized policies and show
that they are at-most a factor of two away from optimal policies in general.
Then, we develop a Lyapunov drift-based max-weight policy that performs better
than randomized policies in practice and show that it is also at-most a factor
of two away from optimal. Next, we derive scaling results that show how AoI
improves in large networks in the presence of correlation. We also show that
for stationary randomized policies, the expression for average AoI is robust to
the way in which the correlation structure is modeled. Finally, for the setting
where correlation parameters are unknown and time-varying, we develop a
heuristic policy that adapts its scheduling decisions by learning the
correlation parameters in an online manner. We also provide numerical
simulations to support our theoretical results.
