In Internet of Things (IoTs), the freshness of system status information is crucial for real-time monitoring and decision-making. This paper studies the transmission scheduling problem in wireless monitoring systems, where information freshness -- typically quantified by the Age of Information (AoI) -- is heavily constrained by limited channel resources and influenced by factors such as the randomness of data arrivals and unreliable wireless channel. Such randomness leads to asynchronous AoI evolution at local sensors and the monitoring center, rendering conventional scheduling policies that rely solely on the monitoring center's AoI inefficient. To this end, we propose a dual-AoI model that captures asynchronous AoI dynamics and formulate the problem as minimizing a long-term time-average AoI function. We develop a scheduling policy based on Markov decision process (MDP) to solve the problem, and analyze the existence and monotonicity of a deterministic stationary optimal policy. Moreover, we derive a low-complexity scheduling policy which exhibits a channel-state-dependent threshold structure. In addition, we establish a necessary and sufficient condition for the stability of the AoI objective. Simulation results demonstrate that the proposed policy outperforms existing approaches.

翻译：在物联网（IoT）系统中，状态信息的新鲜度对于实时监测与决策至关重要。本文研究无线监测系统中的传输调度问题，其中信息新鲜度——通常以信息年龄（AoI）度量——受到有限信道资源的严格约束，并受数据到达随机性及无线信道不可靠性等因素影响。此类随机性导致本地传感器与监测中心的AoI演化异步，使得仅依赖监测中心AoI的传统调度策略效率低下。为此，我们提出一种刻画异步AoI动态的双AoI模型，并将问题形式化为最小化长期时间平均AoI函数。我们基于马尔可夫决策过程（MDP）构建调度策略以求解该问题，并分析了确定性稳态最优策略的存在性与单调性。此外，我们推导出一种具有低计算复杂度且呈现信道状态依赖阈值结构的调度策略。同时，我们建立了AoI目标稳定性的充要条件。仿真结果表明，所提策略性能优于现有方法。