电信科学 ›› 2014, Vol. 30 ›› Issue (9): 92-99.doi: 10.3969/j.issn.1000-0801.2014.09.013

• 研究与开发 • 上一篇    下一篇

WSN中基于多分辨率和压缩感知的数据融合方案

赵建军1,王怀宇1,赵泽阳1,陈生昌2   

  1. 1 保定学院信息技术系 保定071000
    2 浙江大学理学院 杭州310058
  • 出版日期:2014-09-20 发布日期:2017-07-05
  • 基金资助:
    国家自然科学基金面上项目

Data Aggregation Scbeme Based on Multi-Resolution and Compressive Sensing in Wireless Sensor Network

Jianjun Zhao1,Huaiyu Wang1,Zeyang Zhao1,Shengchang Chen2   

  1. 1 Department of Information Technology, Baoding College, Baoding 071000, China
    2 The Science College, Zhejiang University, Hangzhou 310058, China
  • Online:2014-09-20 Published:2017-07-05

摘要:

当前基于压缩感知的传感器网络数据融合方案中,不论数据字段有何特征,均假设网络具有固定而均匀的压缩阈值,从而导致数据通信量过高,能耗浪费较大。提出一种基于多分辨率和压缩感知的数据融合方案。首先,对传感器网络进行配置,以生成多个层次类型不同的簇结构,用于过渡式数据收集,在该结构上,最低层的叶节点只传输原始数据,其他层的数据收集簇进行压缩采样;然后将其测量值向上发送,当母数据收集簇收到测量值时,利用基于反向DCT和DCT模型的CoSaMP算法恢复原始数据;最后,在SIDnet-SWANS平台上部署了该方案,并在不同的二维随机部署传感器网络规模下进行了测试。实验结果表明,随着分层位置的变化,大部分节点的能耗均显著降低,与NCS方案相比,能耗下降50%~77%,与HCS方案相比,能耗下降37%~70%。

关键词: 无线传感器网络, 数据融合, 多分辨率, 压缩感知, 簇, 能耗

Abstract:

A data aggregation scheme based on multi-resolution with compressed sensing was proposed. Firstly, the network was configured to achieve the multiple-level and the different types of cluster structure for intermediate data collection, on this structure, the leaf nodes in the lowest level only transmit the raw data. The collecting clusters in other levels perform the compressed sampling and then transmit them to their parent cluster heads. When parent collecting clusters receive random measurements, they use inverse DCT and DCT model based CoSaMP algorithm to recover the original data. The proposed scheme was implemented on a SIDnet-SWANS simulation platform and test different sizes of two-dimensional randomly deployed sensor network. The experiment results show that the substantial energy savings are reported for a large portion of sensors on the different hierarchical positions, ranging from 50% to 77% when compared with NCS, and from 37% to 70% when compared with HCS.

Key words: wireless sensor network, data aggregation, multi-resolution, compressive sensing, cluster, energy consumption

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