Real-time status updating applications increasingly rely on networks of devices and edge nodes to maintain data freshness, as quantified by the age of information (AoI) metric. Given that edge computing nodes exhibit uncertain and time-varying dynamics, it is essential to identify the optimal edge node with high confidence and sample efficiency, even without prior knowledge of these dynamics, to ensure timely updates. To address this challenge, we introduce the first best arm identification (BAI) problem aimed at minimizing the long-term average AoI under a fixed confidence setting, framed within the context of a restless multi-armed bandit (RMAB) model. In this model, each arm evolves independently according to an unknown Markov chain over time, regardless of whether it is selected. To capture the temporal trajectories of AoI in the presence of unknown restless dynamics, we develop an age-aware LUCB algorithm that incorporates Markovian sampling. Additionally, we establish an instance-dependent upper bound on the sample complexity, which captures the difficulty of the problem as a function of the underlying Markov mixing behavior. Moreover, we derive an information-theoretic lower bound to characterize the fundamental challenges of the problem. We show that the sample complexity is influenced by the temporal correlation of the Markov dynamics, aligning with the intuition offered by the upper bound. Our numerical results show that, compared to existing benchmarks, the proposed scheme significantly reduces sampling costs, particularly under more stringent confidence levels.

翻译：实时状态更新应用日益依赖于设备与边缘节点构成的网络来维持数据新鲜度，该特性通过信息年龄（AoI）指标进行量化。鉴于边缘计算节点表现出不确定且时变的动态特性，即使在没有这些动态先验知识的情况下，也必须以高置信度和采样效率识别最优边缘节点，从而确保更新的时效性。为应对这一挑战，我们首次提出了在固定置信度设置下最小化长期平均AoI的最优臂识别（BAI）问题，该问题被构建于动态多臂赌博机（RMAB）模型的框架内。在此模型中，每个臂随时间根据未知的马尔可夫链独立演化，无论其是否被选择。为捕捉存在未知动态演化特性时AoI的时间轨迹，我们开发了一种融合马尔可夫采样的年龄感知LUCB算法。此外，我们建立了样本复杂度的实例相关上界，该上界通过底层马尔可夫混合行为函数刻画了问题的难度。进一步，我们推导了信息论下界以描述该问题的本质挑战。我们证明样本复杂度受马尔可夫动态时间相关性的影响，这与上界所揭示的直观理解相一致。数值结果表明，相较于现有基准方法，所提方案显著降低了采样成本，在更严格的置信水平下效果尤为明显。