Age of information (AoI) captures the freshness of information and has been used broadly as an important performance metric in big data analytics in the Internet of Things (IoT). We consider a general scenario where a meaningful piece of information consists of multiple packets and the information is not complete until all related packets have been correctly received. Minimizing AoI in this general scenario is challenging in both scheduling algorithm design and theoretical analysis, because we need to track the history of received packets before a complete piece of information can be updated. We first analyse the necessary condition for optimal scheduling, based on which we present an optimal scheduling method. The optimal solution, however, has high time complexity. To address the problem, we investigate the problem with a special type of learning, i.e., learning in restless multi-armed bandits (RMAB), and propose a Whittle index-based scheduling method. We also propose a new transmission strategy based on erasure codes to improve the performance of scheduling policies in lossy networks. Performance evaluation results demonstrate that our solution outperforms other baseline policies such as greedy policy and naïve Whittle index policy in both lossless and lossy networks.

中文翻译：

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最小化IoT网络中多数据包信息使用期限的Whittle索引方法

信息时代（AoI）捕捉信息的新鲜度，并已广泛用作物联网（IoT）中大数据分析中的重要性能指标。我们考虑一种通用情况，其中有意义的一条信息由多个数据包组成，并且直到所有相关数据包都已被正确接收后，信息才完整。在调度算法设计和理论分析中，在这种一般情况下最小化AoI都是具有挑战性的，因为在更新完整的信息之前，我们需要跟踪接收到的数据包的历史记录。我们首先分析最优调度的必要条件，然后在此基础上提出一种最优调度方法。然而，最佳解决方案具有很高的时间复杂度。为了解决这个问题，我们通过一种特殊的学习方式（即在不安定的多臂土匪（RMAB）中学习）来研究该问题，并提出了一种基于Whittle索引的调度方法。我们还提出了一种基于擦除码的新传输策略，以提高有损网络中调度策略的性能。绩效评估结果表明，在无损和有损网络中，我们的解决方案均优于其他基准策略，例如贪婪策略和幼稚的Whittle索引策略。