Low-Power Status Updates via Sleep-Wake Scheduling
Low-Power Status Updates via Sleep-Wake Scheduling
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DOI：
10.1109/tnet.2021.3081102
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发表时间：
2021-02
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影响因子：
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通讯作者：
A. Bedewy;Yin Sun;Rahul Singh;N. Shroff
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作者：
A. Bedewy;Yin Sun;Rahul Singh;N. Shroff
我们考虑对从大量低功耗源发送到公共接入点的状态更新的新鲜度进行优化的问题。源节点利用载波侦听来减少冲突，并采用异步睡眠 - 唤醒调度策略以实现目标网络寿命（例如，10年）。我们使用信息年龄（AoI）来衡量状态更新的新鲜度，并设计睡眠 - 唤醒参数，以便在每个源的电池寿命约束条件下最小化源的加权和峰值AoI。当侦听时间（即载波侦听的持续时间）为零时，这个睡眠 - 唤醒设计问题可以通过一个两层嵌套的凸优化过程来解决；然而，对于正的侦听时间，该问题是非凸的。我们设计了一种低复杂度的解决方案来解决这个问题，并证明对于较短的实际侦听时间，该解决方案与最优AoI性能的差距很小。当状态更新分组的平均传输时间未知时，我们设计了一种强化学习算法，该算法以一种“高效的方式”自适应地执行以下两个任务：a）它学习未知参数，b）它还生成有效的控制来做出信道接入决策。我们通过量化其“遗憾”（即其平均性能与知道平均传输时间的控制器的平均性能之间的次优差距）来分析其性能。我们的数值和NS - 3仿真结果表明，我们的解决方案确实能够延长信息源的电池寿命，同时提供有竞争力的AoI性能。
We consider the problem of optimizing the freshness of status updates that are sent from a large number of low-power sources to a common access point. The source nodes utilize carrier sensing to reduce collisions and adopt an asynchronized sleep-wake scheduling strategy to achieve a target network lifetime (e.g., 10 years). We use age of information (AoI) to measure the freshness of status updates, and design sleep-wake parameters for minimizing the weighted-sum peak AoI of the sources, subject to per-source battery lifetime constraints. When the sensing time (i.e., the time duration of carrier sensing) is zero, this sleep-wake design problem can be solved by resorting to a two-layer nested convex optimization procedure; however, for positive sensing times, the problem is non-convex. We devise a low-complexity solution to solve this problem and prove that, for practical sensing times that are short, the solution is within a small gap from the optimum AoI performance. When the mean transmission time of status-update packets is unknown, we devise a reinforcement learning algorithm that adaptively performs the following two tasks in an “efficient way”: a) it learns the unknown parameter, b) it also generates efficient controls that make channel access decisions. We analyze its performance by quantifying its “regret”, i.e., the sub-optimality gap between its average performance and the average performance of a controller that knows the mean transmission time. Our numerical and NS-3 simulation results show that our solution can indeed elongate the batteries lifetime of information sources, while providing a competitive AoI performance.