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Energy efficient information gathering in wireless sensor networks using compressive sensing

机译：使用压缩感测的无线传感器网络中的节能信息

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Wireless sensor networks are resource constrained with less memory space, limited power supply, processing speed and availability of bandwidth for communication. Information gathering is the fundamental task of wireless sensor networks. However due to enormous deployment of sensors, a large amount of data is generated by these sensor networks. Processing and transportation of such a huge data increase energy consumption of sensor nodes along with increase in network traffic. Analysis and storage of large data becomes complex. Compressive sensing provides a new paradigm for efficient information gathering in wireless sensor networks. Compressive sensing (CS) generates a sparse signal of few nonzero samples from the original signal at sub Nyquist sampling rate where reconstruction of original signal is possible even with few sparse samples. Thus, the necessary and more accurate information can be obtained from the data gathered by wireless sensor networks with less number of samples. In this paper we investigate the effect of different sampling rate at cluster head depending upon their location from the sink. Hence, we can save significant energy by varying overall sampling rate at the cluster head in wireless sensor network.
机译：无线传感器网络是资源限制，内存空间较少，电源有限，处理速度和带宽的可用性进行通信。信息收集是无线传感器网络的基本任务。然而，由于传感器的巨大部署，这些传感器网络产生了大量数据。这种巨大数据的处理和运输增加了传感器节点的能量消耗以及网络流量的增加。大数据的分析和存储变得复杂。压缩检测为无线传感器网络中的有效信息提供了一种新的范例。压缩检测（CS）从原始信号产生少数非零样品的稀疏信号，其中亚奈奎斯特采样率，即使有很少的稀疏样本也是可能的原始信号的重建。因此，可以从无线传感器网络收集的数据从具有较少数量的样本来获得必要和更准确的信息。在本文中，我们根据从水槽的位置调查不同采样率在簇头处的影响。因此，我们可以通过在无线传感器网络中的集群头处改变整体采样率来节省显着的能量。

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