Sampling for data freshness optimization: Non-linear age functions
Sampling for data freshness optimization: Non-linear age functions
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DOI：
10.1109/jcn.2019.000035
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发表时间：
2018-12
影响因子：
3.6
通讯作者：
Yin Sun;Benjamin Cyr
中科院分区：
文献类型：
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作者：
Yin Sun;Benjamin Cyr
在本文中，我们研究如何在数据源处进行采样，以提高远程接收端所接收数据样本的新鲜度。我们使用信息年龄的非线性函数来衡量数据新鲜度，并对非线性年龄函数及其应用进行了综述。我们研究了采样器设计问题，以优化这些数据新鲜度指标，即使存在采样率约束。这个采样问题被表述为一个具有可能不可数状态空间的约束马尔可夫决策过程（MDP）。我们完整地刻画了这个MDP的最优解：最优采样策略是确定性或随机阈值策略，其中阈值和随机化概率是根据MDP的最优目标值和采样率约束来刻画的。最优采样策略可以通过二分搜索来计算，从而避免了维度灾难。这些年龄最优性结果适用于：（i）由信息年龄的单调函数表示的一般数据新鲜度指标；（ii）排队服务器的一般服务时间分布；（iii）连续时间和离散时间采样问题；（iv）有采样率约束和无采样率约束的采样问题。数值结果表明，最优采样策略可能比零等待采样和经典的均匀采样要好得多。
In this paper, we study how to take samples at a data source for improving the freshness of received data samples at a remote receiver. We use non-linear functions of the age of information to measure data freshness, and provide a survey of non-linear age functions and their applications. The sampler design problem is studied to optimize these data freshness metrics, even when there is a sampling rate constraint. This sampling problem is formulated as a constrained Markov decision process (MDP) with a possibly uncountable state space. We present a complete characterization of the optimal solution to this MDP: The optimal sampling policy is a deterministic or randomized threshold policy, where the threshold and the randomization probabilities are characterized based on the optimal objective value of the MDP and the sampling rate constraint. The optimal sampling policy can be computed by bisection search, and the curse of dimensionality is circumvented. These age optimality results hold for (i) general data freshness metrics represented by monotonic functions of the age of information, (ii) general service time distributions of the queueing server, (iii) both continuous-time and discrete-time sampling problems, and (iv) sampling problems both with and without the sampling rate constraint. Numerical results suggest that the optimal sampling policies can be much better than zero-wait sampling and the classic uniform sampling.