  In this paper, a real-time Internet of Things (IoT) monitoring system is
considered in which multiple IoT devices must transmit timely updates on the
status information of a common underlying physical process to a common
destination. In particular, a real-world IoT scenario is considered in which
multiple (partially) observed status information by different IoT devices are
required at the destination, so that the real-time status of the physical
process can be properly re-constructed. By taking into account such correlated
status information at the IoT devices, the problem of IoT device scheduling is
studied in order to jointly minimize the average age of information (AoI) at
the destination and the average energy cost at the IoT devices. Particularly,
two types of IoT devices are considered: Type-I devices whose status updates
randomly arrive and type-II devices whose status updates can be
generated-at-will with an associated sampling cost. This stochastic problem is
formulated as an infinite horizon average cost Markov decision process (MDP).
The optimal scheduling policy is shown to be threshold-based with respect to
the AoI at the destination, and the threshold is non-increasing with the
channel condition of each device. For a special case in which all devices are
type-II, the original MDP can be reduced to an MDP with much smaller state and
action spaces. The optimal policy is further shown to have a similar
threshold-based structure and the threshold is non-decreasing with an energy
cost function of the devices. Simulation results illustrate the structure of
the optimal policy and show the effectiveness of the optimal policy compared
with a myopic baseline policy.
