  We consider IoT sensor network where multiple sensors are connected to
corresponding destination nodes via a relay. Thus, the relay schedules sensors
to sample and destination nodes to update. The relay can select multiple
sensors and destination nodes in each time. In order to minimize average
weighted sum AoI, joint optimization of sampling and updating policy of the
relay is investigated. For errorless and symmetric case where weights are
equally given, necessary and sufficient conditions for optimality is found.
Using this result, we obtain that the minimum average sum AoI in a closed-form
expression which can be interpreted as fundamental limit of sum AoI in a single
relay network. Also, for error-prone and symmetric case, we have proved that
greedy policy achieves the minimum average sum AoI at the destination nodes.
For general case, we have proposed scheduling policy obtained via reinforcement
learning.
