Optimizing Average Age of Information in Industrial IoT Systems Under Delay Constraint
Heng Wang, Xin Xie, Jingqi Yang
Abstract
Age of information (AoI) is a new metric that can measure the data freshness of the industrial Internet of things (IIoT) systems. Focusing on a hybrid scenario where periodic and random sampling devices exist simultaneously, we investigate AoI-aware scheduling schemes under deterministic delay constraint of devices with periodic sampling in noisy channels. We first consider that the probability of successful delivery of data obeys a known fixed probability, and develop a dynamic scheduling scheme utilizing the slot-based Lyapunov drift framework. Second, in the case where the prior knowledge of the probability of successful data delivery is unknown, we introduce deep reinforcement learning (DRL) to learn the model-free scheduling and propose a scheduling policy based on the dueling deep Q network (D3QN). Numerical results show that the proposed Lyapunov policy and D3QN policy can minimize the average AoI while subjecting to the delay constraint.