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Distributed correlated data gathering in wireless sensor networks via compressed sensing

机译:通过压缩传感在无线传感器网络中收集分布式相关数据

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This paper considers energy efficient distributed data gathering methods for correlated data field monitoring in multi-hop wireless sensor networks (WSNs) via compressed sensing (CS). This signal compression and acquisition method can be used to exploit the inherent temporal and spatial correlation of the sensor signals. Many existing CS-based data gathering methods exploit correlation structures only in one signal dimension in WSNs. Thus, we propose a distributed method which utilizes the joint correlation pattern of a multi-dimensional WSN signal ensemble via Kronecker sparsity basis. Numerical results how that the proposed method can significantly reduce the data traffic in correlated data gathering multi-hop WSNs.
机译:本文考虑了通过压缩感知(CS)在多跳无线传感器网络(WSN)中进行相关数据字段监视的节能分布式数据收集方法。该信号压缩和采集方法可用于开发传感器信号的固有时间和空间相关性。许多现有的基于CS的数据收集方法仅在WSN的一个信号维度中利用相关结构。因此,我们提出了一种分布式方法,该方法通过Kronecker稀疏度基础利用多维WSN信号集合的联合相关模式。数值结果表明,该方法可以显着减少相关数据收集多跳WSN中的数据流量。

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