Optimal Scheduling Policy for Minimizing Age of Information With a Relay

We investigate Age of Information (AoI) in an Internet of Things (IoT) sensor network where a single relay terminal connects multiple IoT sensors to their corresponding destination nodes. In order to minimize average weighted sum AoI, joint optimization of sampling and updating policy of a relay is studied. For error-free and symmetric case where weights are identical, the necessary and sufficient condition for optimal policy is figured out. We also obtain the minimum average sum AoI in a closed-form expression which can be interpreted as the fundamental limit of sum AoI in a single relay network. Moreover, we prove that the greedy policy is optimal for minimizing the average sum AoI at the destination nodes in the error-prone symmetric network. For general case where weights are arbitrarily given, we propose a scheduling policy obtained via deep reinforcement learning.