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Tree-based energy-efficient data gathering in wireless sensor networks deploying compressive sensing

机译:部署压缩感测的无线传感器网络中基于树的节能数据收集

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Compressive sensing (CS) provides a new paradigm for data collection in wireless sensor networks (WSNs). In this paper, we continue to exploit the integration between CS and tree-based data gathering in WSNs. Based on a tree formed as down-stream generations from the sink or base-station (BS), each parent node stores its children's readings and the corresponding measurement vectors through the data collecting process. All sensors send their own readings only once to their parents. The parent nodes generate measurements based on the projection matrix and then forward a certain number of measurements required to the upper nodes or the BS. The CS recovery algorithm implemented at the BS reconstructs precise readings from all sensors. This significantly reduces a certain number of transmissions in routing. Two additional ideas are suggested related to sensor transmission range and the probability of non-zero elements in the projection matrix for the purpose of reducing power consumption for the networks.
机译:压缩感测(CS)为无线传感器网络(WSN)中的数据收集提供了新的范例。在本文中,我们将继续利用CS和WSN中基于树的数据收集之间的集成。基于从接收器或基站(BS)作为下游代形成的树,每个父节点通过数据收集过程存储其子级的读数和相应的测量向量。所有传感器仅向父母发送一次自己的读数。父节点基于投影矩阵生成测量,然后将所需的一定数量的测量转发到上层节点或BS。在BS实施的CS恢复算法可从所有传感器重建精确的读数。这显着减少了路由选择中的一定数量的传输。为了减少网络的功耗,建议了另外两个与传感器传输范围和投影矩阵中非零元素的概率有关的想法。

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