Zheng Haifeng, Li Jiayin, Feng Xinxin, Guo Wenzhong, Chen Zhonghui, Xiong Neal
College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China.
Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China.
Sensors (Basel). 2017 Nov 8;17(11):2575. doi: 10.3390/s17112575.
Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs .
压缩感知（CS）为无线传感器网络（WSN）中的数据收集提供了一种节能范例。然而，现有的利用压缩感知进行时空数据收集的工作仅考虑基于多跳中继或基于多个随机游走的方法。在本文中，我们利用移动模式进行时空数据收集，并通过采用带延迟接受的Metropolis-Hastings算法提出了一种新颖的移动数据收集方案，这是一种改进的随机游走算法，用于移动收集器从传感场收集数据。所提出的方案通过允许移动收集器沿着随机路由路径从一小部分随机选择的节点收集时间压缩测量值，利用克罗内克压缩感知（KCS）来处理传感数据的时空相关性。更重要的是，从理论角度我们证明了由所提出的方案为时空可压缩信号构造的等效传感矩阵可以满足KCS模型的性质。仿真结果表明，与其他现有方案相比，所提出的方案不仅可以显著降低通信成本，还能提高移动数据收集的恢复精度。特别是，我们还表明所提出的方案在各种丢包情况下的不可靠无线环境中具有鲁棒性。所有这些表明所提出的方案可以成为WSN中数据收集应用的一种有效替代方案。
