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.

翻译：我们考虑物联网传感器网络，其中多个传感器通过中继连接到对应的目标节点。因此，中继负责调度传感器进行采样及目标节点进行更新。在每个时隙中，中继可选择多个传感器和目标节点。为最小化加权平均信息年代，本文研究了中继采样与更新策略的联合优化问题。针对无差错且权重均等对称的场景，提出了最优性的充要条件。基于该结果，我们推导出最小平均信息年代总和的闭式表达式，该表达式可解释为单中继网络中信息年代总和的基本极限。此外，针对易出错且对称的场景，我们证明了贪婪策略可在目标节点处实现最小平均信息年代总和。对于一般场景，我们提出了通过强化学习获得的调度策略。