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A high quality event capture scheme for WSN-based structural health monitoring

机译:基于WSN的结构健康监控的高质量事件捕获方案

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In recent years, there has been an increasing interest in the adoption of wireless sensor networks (WSNs) for structural health monitoring (SHM). However, considering the large amount of sampled data, limited power supply and wireless bandwidth of WSNs, it is generally not possible for sensor nodes to monitor structural condition continuously especially for long-term SHM. From SHM perspective, it is highly desirable to collect data during the occurrence of some certain kinds of events such as earthquakes, large wind, etc, since data collected during these periods are more informative for damage detection purpose. However, these events in SHM occur infrequently and if happen, only last for a very short period of time. To effectively capture these short events using energy-limited wireless sensor nodes is a challenging task that has not been addressed in literature. In this paper, we propose a fast and reliable scheme to capture these short-term events. In terms of hardware, the radio-triggered unit and the vibration-triggered unit are designed in our motes. From software perspective, we develop the event capture scheme to realize fast, reliable and energy-efficient event detection under noisy environment. We mainly study the very first problem of the scheme: sentry selection. The optimization problem is formulated and we show that the problem is a general case of the classical k-center problem. Then we propose a greedy algorithm to solve this problem, and conduct simulation to test the effectiveness of the proposed algorithm.
机译:近年来,人们越来越关注采用无线传感器网络(WSN)进行结构健康监测(SHM)。但是,考虑到大量采样数据,有限的电源和WSN的无线带宽,传感器节点通常不可能连续监视结构状态,尤其是对于长期SHM。从SHM的角度来看,非常需要在某些特定类型的事件(例如地震,大风等)发生时收集数据,因为在这些时间段内收集的数据对于损坏检测而言更具参考价值。但是,SHM中的这些事件很少发生,并且如果发生,只会持续很短的时间。使用能量受限的无线传感器节点有效捕获这些短事件是一项艰巨的任务,文献中尚未解决。在本文中,我们提出了一种快速可靠的方案来捕获这些短期事件。在硬件方面,无线电触发单元和振动触发单元是在我们的节点上设计的。从软件角度,我们开发了事件捕获方案,以在嘈杂的环境下实现快速,可靠和节能的事件检测。我们主要研究该方案的第一个问题:哨兵选择。提出了优化问题,我们证明了该问题是经典k中心问题的一般情况。然后我们提出了一种贪婪算法来解决该问题,并进行仿真以验证该算法的有效性。

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