Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach
Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach
复制标题
无线传感器网络中压缩感知的数据收集：基于随机游走的方法
DOI：
10.1109/tpds.2014.2308212
复制
发表时间：
2015-01-01
影响因子：
5.3
通讯作者：
Xiao, Shilin
中科院分区：
文献类型：
--
作者：
Zheng, Haifeng;Yang, Feng;Xiao, Shilin
在本文中，我们研究了无线传感器网络（WSNs）中的压缩感知（CS）数据收集问题。不同于传统的方法，这需要均匀采样在传统的CS理论，我们提出了一种随机游走算法的数据收集在无线传感器网络。然而，这种方法将符合网络中的路径约束，并导致测量的非均匀选择。目前还不知道这种非均匀的方法是否可以用于CS恢复稀疏信号在无线传感器网络。在本文中，从CS理论和图论的角度来看，我们提供的数学基础，允许随机测量，以随机游走为基础的方式收集。我们发现，我们的随机行走算法构造的随机矩阵可以满足扩展图的扩展性质。理论分析表明，在n个节点的随机几何网络中，当m = O（klog（n=k））次独立随机游动且每次游动的长度为t = O（n=k）时，可以用`1最小化译码算法恢复k-稀疏信号.我们还进行了模拟，以证明所提出的计划的有效性。仿真结果表明，我们提出的方案可以显着降低通信成本相比，传统的计划，使用密集随机投影和稀疏随机投影，表明我们的计划可以是一个更实用的替代数据收集应用在无线传感器网络。
In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.