Scheduling Approaches for Joint Optimization of Age and Delay in Industrial Wireless Networks

In industrial wireless networks (IWNs), age of information (AoI) and delay are two significant metrics to measure data freshness and delivery timeliness. In this article, an IWN system with delay-sensitive and normal data is considered. To perform a joint optimization of the average AoI and the deadline-related overdue rate, we investigate the scheduling policy under time-varying channels. With the channel state knowledge available, we evaluate the expected gain obtained with the assumption that whether the data are scheduled or not for each sensor node, and develop a low-complexity scheduling policy. Under the hypothesis that channel state knowledge is not available, we utilize the model-free learning property of dueling double deep Q-network (D3QN) for the learning of scheduling policy and design a weighted expert knowledge-based exploration scheme that can achieve a higher convergence speed compared to the classical D3QN. Simulation results show the tradeoff between AoI and delay and demonstrate that the two proposed policies outperform existing state-of-the-art algorithms.