Graph spectral compressed sensing for sensor networks

Xiaofan Zhu, M. Rabbat
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引用次数: 56

Abstract

Consider a wireless sensor network with N sensor nodes measuring data which are correlated temporally or spatially. We consider the problem of reconstructing the original data by only transmitting M ≪ N sensor readings while guaranteeing that the reconstruction error is small. Assuming the original signal is “smooth” with respect to the network topology, our approach is to gather measurements from a random subset of nodes and then interpolate with respect to the graph Laplacian eigenbasis, leveraging ideas from compressed sensing. We propose algorithms for both temporally and spatially correlated signals, and the performance of these algorithms is verified using both synthesized data and real world data. Significant savings are made in terms of energy resources, bandwidth, and query latency.
用于传感器网络的图谱压缩感知
考虑一个具有N个传感器节点的无线传感器网络,这些节点测量的数据在时间或空间上是相关的。我们考虑通过只传送M≪N传感器读数来重建原始数据的问题,同时保证重建误差小。假设原始信号相对于网络拓扑是“平滑的”,我们的方法是从节点的随机子集中收集测量值,然后利用压缩感知的思想,对图拉普拉斯特征基进行插值。我们提出了时间和空间相关信号的算法,并使用合成数据和真实世界的数据验证了这些算法的性能。在能源、带宽和查询延迟方面可以显著节省。
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