arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.05364cs.ITmath.IT
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无线传感器网络信息受限动态控制中的功率最小化

Power Minimization for Age of Information Constrained Dynamic Control in Wireless Sensor Networks

Mohammad Moltafet, Markus Leinonen, Marian Codreanu, Nikolaos Pappas

摘要:

我们考虑一个系统,多个传感器及时沟通各种随机过程的信息汇。传感器共享正交子信道以状态更新分组的形式发送这样的信息。中央控制器可以控制传感器的采样动作,以在发送功率消耗和信息新鲜度之间进行权衡,该信息新鲜度由信息年龄(AoI)量化。我们共同优化每个传感器的采样动作,发射功率分配,和子信道分配,以最小化所有传感器的平均总发射功率,每个传感器的最大平均AoI约束。为了解决这个问题,我们开发了一个动态控制算法,使用李雅普诺夫漂移加惩罚的方法,并提供算法的最优性分析。根据李雅普诺夫漂移加惩罚方法,为了解决主问题,我们需要在每个时隙中解决一个优化问题,这是一个混合整数非凸优化问题。我们提出了一个低复杂度的次优解决方案,提供接近最优的性能,并评估解决方案的计算复杂性。数值结果表明,所提出的动态控制算法的性能和性能的次优解的每槽优化问题与系统的不同参数。结果表明,所提出的动态控制算法实现了超过$60~\%$节省的平均总发射功率相比,基线策略。

英文摘要:

We consider a system where multiple sensors communicate timely information about various random processes to a sink. The sensors share orthogonal sub-channels to transmit such information in the form of status update packets. A central controller can control the sampling actions of the sensors to trade-off between the transmit power consumption and information freshness which is quantified by the Age of Information (AoI). We jointly optimize the sampling action of each sensor, the transmit power allocation, and the sub-channel assignment to minimize the average total transmit power of all sensors subject to a maximum average AoI constraint for each sensor. To solve the problem, we develop a dynamic control algorithm using the Lyapunov drift-plus-penalty method and provide optimality analysis of the algorithm. According to the Lyapunov drift-plus-penalty method, to solve the main problem we need to solve an optimization problem in each time slot which is a mixed integer non-convex optimization problem. We propose a low-complexity sub-optimal solution for this per-slot optimization problem that provides near-optimal performance and we evaluate the computational complexity of the solution. Numerical results illustrate the performance of the proposed dynamic control algorithm and the performance of the sub-optimal solution for the per-slot optimization problems versus the different parameters of the system. The results show that the proposed dynamic control algorithm achieves more than $60~\%$ saving in the average total transmit power compared to a baseline policy.

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