Energy-Efficient Data Recovery via Greedy Algorithm for Wireless Sensor Networks
Energy-Efficient Data Recovery via Greedy Algorithm for Wireless Sensor Networks
复制标题
DOI：
10.1155/2016/7256396
复制
发表时间：
2016-02
影响因子：
2.3
通讯作者：
Z. Zou;Ze-ting Li;Shu Shen;Ru-chuan Wang
中科院分区：
文献类型：
--
作者：
Z. Zou;Ze-ting Li;Shu Shen;Ru-chuan Wang
在大规模无线传感器网络(WSNs)中，不断增加的能耗和不断增加的数据流量已经成为突出的问题。压缩感知(CS)可以通过采集少量样本来恢复数据，具有较高的能量效率。一般的CS理论在应用于无线传感器网络时，由于其基于L 1的传统凸优化算法的高度复杂性以及其高斯随机观测矩阵所需的大存储空间，在应用于无线传感器网络时具有一定的局限性。因此，我们提出了一种新的解决方案，允许在实际的无线传感器网络场景中使用CS进行压缩采样和在线恢复大数据集。采用基于L 0的贪婪算法进行无线传感器网络数据恢复，并结合新设计的基于LEACH分簇算法的测量矩阵，提出了一种压缩采样在线恢复数据采集框架(DAFCSOR)。此外，我们还研究了DAF_CSOR下的三种不同的贪婪算法。评估实验结果表明，稀疏度自适应DAF_CSOR在恢复精度方面是相对最优的。与传统方法相比，DAF_CSOR在整体能耗和网络寿命方面表现出一定的优势。
Accelerating energy consumption and increasing data traffic have become prominent in large-scale wireless sensor networks (WSNs). Compressive sensing (CS) can recover data through the collection of a small number of samples with energy efficiency. General CS theory has several limitations when applied to WSNs because of the high complexity of its l 1 -based conventional convex optimization algorithm and the large storage space required by its Gaussian random observation matrix. Thus, we propose a novel solution that allows the use of CS for compressive sampling and online recovery of large data sets in actual WSN scenarios. The l 0 -based greedy algorithm for data recovery in WSNs is adopted and combined with a newly designed measurement matrix that is based on LEACH clustering algorithm integrated into a new framework called data acquisition framework of compressive sampling and online recovery (DAF_CSOR). Furthermore, we study three different greedy algorithms under DAF_CSOR. Results of evaluation experiments show that the proposed sparsity-adaptive DAF_CSOR is relatively optimal in terms of recovery accuracy. In terms of overall energy consumption and network lifetime, DAF_CSOR exhibits a certain advantage over conventional methods.