  In this paper, we consider a multiuser uplink status update system, where a
monitor aims to timely collect randomly generated status updates from multiple
end nodes through a shared wireless channel. We adopt the recently proposed
metric, termed age of information (AoI), to quantify the information timeliness
and freshness. Due to the random generation of the status updates at the end
node side, the monitor only grasps a partial knowledge of the status update
arrivals. Under such a practical scenario, we aim to address a fundamental
multiuser scheduling problem: how to schedule the end nodes to minimize the
network-wide AoI? To solve this problem, we formulate it as a partially
observable Markov decision process (POMDP), and develop a dynamic programming
(DP) algorithm to obtain the optimal scheduling policy. By noting that the
optimal policy is computationally prohibitive, we further design a
low-complexity myopic policy that only minimizes the one-step expected reward.
Simulation results show that the performance of the myopic policy can approach
that of the optimal policy, and is better than that of the baseline policy.
