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Energy Efficient Sleep/Wake Scheduling for Multi-hop Sensor Networks: Non-convexity and Approximation Algorithm

机译:多跳传感器网络的节能睡眠/唤醒调度:非凸性和近似算法

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We study sleep/wake scheduling for low duty cycle sensor networks. Our work is different from prior work in that we explicitly consider the effect of synchronization error in the design of the sleep/wake scheduling algorithm. In our previous work, we have studied sleep/wake scheduling for single hop communications, e.g., intra-cluster communications between a cluster head and cluster members. We showed that the there is an inherent trade-off between energy consumption and message delivery performance (defined as the message capture probability). We proposed an optimal sleep/wake scheduling algorithm, which satisfies a message capture probability threshold (assumed to be given) with minimum energy consumption. In this work, we consider multi-hop communications. We remove the previous assumption that the capture probability threshold is already given, and study how to decide the per-hop capture probability thresholds to meet the Quality of Services (QoS) requirements of the application. In many sensor network applications, the QoS is decided by the amount of data delivered to the base station(s), i.e., the multi-hop delivery performance. We formulate an optimization problem, which aims to set the capture probability threshold at each hop such that the network lifetime is maximized, while the multi-hop delivery performance is guaranteed. The problem turns out to be non-convex and hard to solve exactly. By investigating the unique structure of the problem and using approximation techniques, we obtain a solution that achieves at least 0.73 of the optimal performance.
机译:我们研究低占空比传感器网络的睡眠/唤醒调度。我们的工作与事先工作不同,因为我们明确考虑了同步误差在睡眠/唤醒调度算法的设计中的影响。在我们以前的工作中,我们研究了单跳通信的睡眠/唤醒调度,例如,群集头和群集成员之间的群集通信。我们表明,能量消耗和消息传递性能之间存在固有的权衡(定义为消息捕获概率)。我们提出了一种最佳的睡眠/唤醒调度算法,其满足消息捕获概率阈值(假设为被给出),其能耗最小。在这项工作中,我们考虑多跳通信。我们删除了上一个假设,即已经给出了捕获概率阈值,并研究了如何确定每跳捕获概率阈值以满足应用程序的服务质量(QoS)要求。在许多传感器网络应用中,QoS由传送到基站的数据量,即多跳输送性能。我们制定了优化问题,该优化问题旨在在每个跳中设置捕获概率阈值,使得网络生命周期最大化,而保证多跳输送性能。问题结果结果是非凸,难以完全解决。通过研究问题的独特结构和使用近似技术,我们获得了一种解决方案,该解决方案实现了至少0.73的最佳性能。

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