Optimal Scheduling Policy for Minimizing Age of Information with a Relay

TL;DR

This paper investigates optimal scheduling policies in IoT relay networks to minimize Age of Information, deriving theoretical limits and proposing reinforcement learning-based solutions for complex scenarios.

Contribution

It provides necessary and sufficient conditions for optimality in symmetric cases and introduces RL-based policies for general cases.

Findings

Closed-form expression for minimum average sum AoI in symmetric errorless case

Greedy policy achieves optimal AoI in symmetric error-prone case

Reinforcement learning-based scheduling policy for general scenarios

Abstract

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.