论文标题
何时从传感器中获取数据以获取最小基于距离的不正确信息度量的数据
When to pull data from sensors for minimum Distance-based Age of incorrect Information metric
论文作者
论文摘要
已经引入了信息时代（AOI），以捕获实时监控应用中新鲜度的概念。但是，在许多情况下，该度量均缺乏，尤其是在量化当前状态和估计状态之间的不匹配时。为了解决这个问题，在本文中，我们采用了不正确的信息指标（AOII）的时代，这些信息指标（AOII）考虑了目的地的来源与知识之间的量化不匹配，同时跟踪新鲜度的影响。我们认为，一个问题中央实体从马尔可夫过程中汲取进化的远程来源中的信息。它在每个时插槽中选择哪些来源应发送其更新。由于调度程序不知道远程源的实际状态，因此每次根据马尔可文源的参数估算AOII的值。它的目标是使AOII功能的时间平均水平尽可能小。为此，我们根据Whittle的索引政策制定计划计划。在某种程度上，我们使用拉格朗日放松方法，并确定二重问题具有最佳的阈值策略。在此基础上，我们计算了Whittle指数的表达方式。最后，我们提供了一些数值结果，以强调与经典AOI度量相比，我们派生的策略的性能。
The age of Information (AoI) has been introduced to capture the notion of freshness in real-time monitoring applications. However, this metric falls short in many scenarios, especially when quantifying the mismatch between the current and the estimated states. To circumvent this issue, in this paper, we adopt the age of incorrect information metric (AoII) that considers the quantified mismatch between the source and the knowledge at the destination while tracking the impact of freshness. We consider for that a problem where a central entity pulls the information from remote sources that evolve according to a Markovian Process. It selects at each time slot which sources should send their updates. As the scheduler does not know the actual state of the remote sources, it estimates at each time the value of AoII based on the Markovian sources' parameters. Its goal is to keep the time average of the AoII function as small as possible. For that purpose, We develop a scheduling scheme based on Whittle's index policy. To that extent, we use the Lagrangian Relaxation Approach and establish that the dual problem has an optimal threshold policy. Building on that, we compute the expressions of Whittle's indices. Finally, we provide some numerical results to highlight the performance of our derived policy compared to the classical AoI metric.
