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Distributed Sensing for High-Quality Structural Health Monitoring Using WSNs

机译：使用WSN进行高质量结构健康监测的分布式传感

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摘要

Due to the low cost and ease of deployment, wireless sensor networks (WSNs) are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered structural health monitoring (SHM) systems. Different from other applications of WSNs such as environmental monitoring, SHM applications are much more data intensive and it is not feasible to stream the raw data back to the server due to the severe bandwidth and energy limitations of low-power sensor networks. In-network processing is a promising approach to address this problem but designing distributed versions for the sophisticated SHM algorithms is much more challenging because SHM algorithms are computationally intensive, and involve data-level collaboration of multiple sensors. In this paper, we select a classical SHM algorithm: the eigen-system realization algorithm (ERA), and propose a few distributed ERAs suitable for WSNs. In particular, we first design a method to incrementally calculate the ERA and then propose three schemes upon which the incremental ERA can be carried out along an Hamiltonian path, along a path in the minimum connected dominating set (MCDS) and along the shortest path tree (SPT). The efficacy of these schemes are demonstrated and compared through both simulation experiment. We believe the proposed schemes can also serve as a guideline when applying WSNs for other applications like SHM which are also data-intensive and involve sophisticated signal processing of collected information.
机译：由于成本低廉且易于部署，无线传感器网络（WSN）逐渐成为传感范式，结构工程领域已开始考虑将其替换为传统的系留结构健康监测（SHM）系统。与WSN的其他应用程序（例如环境监测）不同，SHM应用程序的数据强度更高，并且由于低功耗传感器网络的严格带宽和能量限制，将原始数据流传输回服务器是不可行的。网络内处理是解决该问题的一种有前途的方法，但是为复杂的SHM算法设计分布式版本更具挑战性，因为SHM算法的计算量很大，并且涉及多个传感器的数据级协作。在本文中，我们选择一种经典的SHM算法：本征系统实现算法（ERA），并提出一些适用于WSN的分布式ERA。特别是，我们首先设计一种增量计算ERA的方法，然后提出三种方案，分别可以沿着哈密顿路径，最小连通支配集（MCDS）中的路径和最短路径树进行增量ERA。 （SPT）。通过两个仿真实验证明并比较了这些方案的有效性。我们相信，当将WSN用于其他应用（例如SHM）时，这些方案也可以作为指导，SHM也是数据密集型的，并且涉及收集信息的复杂信号处理。

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