The emerging mission-critical Internet of Things (IoT) play a vital role in remote healthcare, haptic interaction, and industrial automation, where timely delivery of status updates is crucial. The Age of Information (AoI) is an effective metric to capture and evaluate information freshness at the destination. A system design based solely on the optimization of the average AoI might not be adequate to capture the requirements of mission-critical applications, since averaging eliminates the effects of extreme events. In this paper, we introduce a Deep Reinforcement Learning (DRL)-based algorithm to improve AoI in mission-critical IoT applications. The objective is to minimize an AoI-based metric consisting of the weighted sum of the average AoI and the probability of exceeding an AoI threshold. We utilize the actor-critic method to train the algorithm to achieve optimized scheduling policy to solve the formulated problem. The performance of our proposed method is evaluated in a simulated setup and the results show a significant improvement in terms of the average AoI and the AoI violation probability compared to the related-work.

翻译：新兴的关键任务物联网在远程医疗、触觉交互和工业自动化中发挥着至关重要的作用，其中状态更新的及时传递至关重要。信息年龄是用于捕获和评估目的地信息新鲜度的有效指标。仅基于平均AoI优化的系统设计可能不足以满足关键任务应用的需求，因为平均化处理消除了极端事件的影响。本文提出了一种基于深度强化学习的算法，用于改善关键任务物联网应用中的AoI。其目标是最小化由平均AoI的加权和与超过AoI阈值的概率组成的AoI指标。我们采用行动者-批评家方法训练算法，以实现优化调度策略来解决所提出的问题。所提出方法的性能在模拟设置中进行了评估，结果显示与现有工作相比，在平均AoI和AoI违规概率方面显著改善。