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首页> 外文期刊>Sensors Journal, IEEE >Multivariated Bayesian Compressive Sensing in Wireless Sensor Networks
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Multivariated Bayesian Compressive Sensing in Wireless Sensor Networks

机译：无线传感器网络中的多元贝叶斯压缩感知

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摘要

Recently, compressive sensing has been studied in wireless sensor networks, which allows an aggregator to recover the desired sparse signal with fewer active sensor nodes. In this paper, we consider heterogeneous sensing environments, where the sensing quality varies due to the differences in the physical environment of each sensor node. We consider a Bayesian compressive sensing approach and propose two efficient algorithms that decrease the number of active sensor nodes while maintaining high performance. Both the selection algorithms aim to reduce the estimation error by minimizing the determinant of the error covariance matrix, which is proportional to the volume of the confidence ellipsoid. The first algorithm is the centralized greedy selection algorithm, which can achieve a nearly optimal solution in terms of the minimum confidence ellipsoid. It can also achieve almost the same level of performance as the combinatorial selection method, but has a lower complexity and outperforms the conventional convex relaxation method. The second algorithm is the decentralized selection algorithm, which is derived by approximating the determinant of the error covariance matrix. Unlike the centralized greedy algorithm, it can be done by each sensor node without heavy overhead or high complexity. Furthermore, we prove that the decentralized selection algorithm becomes equivalent to the centralized greedy algorithm as the number of sensor nodes increases. Our simulation results show that the centralized greedy selection algorithm provides the best performance while the decentralized algorithm performs nearly as well as the centralized algorithm as the number of sensor nodes increases.
机译：近来，在无线传感器网络中已经研究了压缩感测，这允许聚合器以较少的活动传感器节点来恢复所需的稀疏信号。在本文中，我们考虑了异构传感环境，其中传感质量由于每个传感器节点的物理环境的差异而有所不同。我们考虑了一种贝叶斯压缩感测方法，并提出了两种有效的算法，这些算法可在保持高性能的同时减少主动传感器节点的数量。两种选择算法均旨在通过使误差协方差矩阵的行列式最小化来减少估计误差，该误差与置信椭圆体的体积成正比。第一种算法是集中式贪婪选择算法，该算法可以在最小置信度椭球方面获得几乎最佳的解决方案。它也可以达到与组合选择方法几乎相同的性能水平，但是具有较低的复杂度，并且优于常规的凸松弛方法。第二种算法是分散选择算法，它是通过近似误差协方差矩阵的行列式而得出的。与集中式贪婪算法不同，它可以由每个传感器节点完成，而不会增加开销或复杂性。此外，我们证明随着传感器节点数量的增加，分散选择算法变得等效于集中式贪婪算法。我们的仿真结果表明，随着传感器节点数量的增加，集中式贪婪选择算法可提供最佳性能，而分散式算法的性能几乎与集中式算法相同。

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