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Networked Computing in Wireless Sensor Networks for Structural Health Monitoring

机译:无线传感器网络中的网络计算用于结构健康监测

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This paper studies the problem of distributed computation over a network of wireless sensors. While this problem applies to many emerging applications, to keep our discussion concrete, we will focus on sensor networks used for structural health monitoring. Within this context, the heaviest computation is to determine the singular value decomposition (SVD) to extract mode shapes (eigenvectors) of a structure. Compared to collecting raw vibration data and performing SVD at a central location, computing SVD within the network can result in significantly lower energy consumption and delay. Using recent results on decomposing SVD, a well-known centralized operation, we seek to determine a near-optimal communication structure that enables the distribution of this computation and the reassembly of the final results, with the objective of minimizing energy consumption subject to a computational delay constraint. We show that this reduces to a generalized clustering problem and establish that it is NP-hard. By relaxing the delay constraint, we derive a lower bound. We then propose an integer linear program (ILP) to solve the constrained problem exactly as well as an approximate algorithm with a proven approximation ratio. We further present a distributed version of the approximate algorithm. We present both simulation and experimentation results to demonstrate the effectiveness of these algorithms .
机译:本文研究了无线传感器网络上的分布式计算问题。尽管此问题适用于许多新兴应用,但为了使我们的讨论更加具体,我们将重点关注用于结构健康状况监视的传感器网络。在这种情况下,最繁重的计算是确定奇异值分解(SVD)以提取结构的模式形状(特征向量)。与收集原始振动数据并在中心位置执行SVD相比,在网络内计算SVD可以显着降低能耗和延迟。利用最近的关于分解SVD(一种众所周知的集中式操作)的结果,我们寻求确定一种接近最佳的通信结构,该结构能够实现此计算的分布和最终结果的重新组合,目的是使计算中的能耗最小化。延迟约束。我们表明,这可以简化为广义聚类问题,并确定它是NP困难的。通过放宽延迟约束,我们得出一个下限。然后,我们提出了一个整数线性程序(ILP)来精确地解决约束问题,并提出了一种经过验证的近似比率的近似算法。我们进一步提出了近似算法的分布式版本。我们提供仿真和实验结果来证明这些算法的有效性。

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