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计算机工程

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WSN集群报告预测及三次折叠压缩转发方法

李岱   

  1. (汉江师范学院 计算机科学系,湖北 十堰 442000)
  • 收稿日期:2016-04-11 出版日期:2017-04-15 发布日期:2017-04-14
  • 作者简介:李岱(1972—),男,副教授、硕士,主研方向为无线传感器网络、大数据。
  • 基金资助:
    湖北省教育科学“十二五”规划项目(2012B454)。

Cluster Report Prediction and Three-fold Compression Forwarding Method in WSN

LI Dai   

  1. (Department of Computer Science,Hanjiang Normal University,Shiyan,Hubei 442000,China)
  • Received:2016-04-11 Online:2017-04-15 Published:2017-04-14

摘要: 针对无线传感器网络中无法构建数据相似的集群以及数据交换通信成本高的问题,提出一种使用数据预测来减少集群开销的方法,节约节点能量以延长无线传感器网络寿命。利用最小通信成本构建数据相似且均匀分布的集群,并基于自适应归一化最小均方根的预测框架减少集群内通信。使用三次折叠压缩方法压缩编码流内的浮点数,利用数据的时空相关性进行压缩。实验结果表明,与多元簇首分簇(CCH)方法、能量效能数据采集(EEDC)方法以及采集预测时空相关(CoPeST)方法相比,提出的方法在保障数据准确度的前提下,有效降低了集群间和集群内部的数据通信量,并显著提高了网络的能量平衡和利用效率。

关键词: 无线传感器网络, 数据预测, 集群, 压缩, 时空相关网络寿命

Abstract: Concerning that many existing methods cannot construct data similar clustering and the cost of data exchange communication is high in Wireless Sensor Network(WSN),the method using data prediction to reduce clustering cost is proposed,which can save energy of the sensor nodes to prolong the lifetime of WSN.Minimum communication cost is used to build a cluster with similar data and uniform distribution.The intra-cluster communication is reduced using the prediction framework based on adaptive-normalized least mean squares.The three-fold compression method is used to compress the floating point number in the coding stream,and it makes full use of the spatial and temporal correlation of the data during the compression process.Compared with method of multiple Cluster Clustering Head(CCH),method of Energy Efficiency Data Collection(EEDC) and method of Collective Prediction exploiting Spatio Temporal (CoPeST),the proposed method achieves significant data reduction in both the intra-cluster and the inter-cluster communications under the premise of ensuring the accuracy of data.And the energy balance and utilization efficiency of the network are greatly improved.

Key words: Wireless Sensor Network(WSN), data prediction, cluster, compression, spatial and temporal correlation, network lifetime

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