ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (3): 493-501.doi: 10.7544/issn1000-1239.2017.20150885

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Data Collection Method in Clustering Network Based on Hybrid Compressive Sensing

Li Zhetao1,2,3, Zang Lang1,3, Tian Shujuan1,3, Li Renfa4   

  1. 1(College of Information Engineering, Xiangtan University, Xiangtan, Hunan 411105); 2(Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (Nanjing University of Posts and Telecommnications), Nanjing 210003); 3(Key Laboratory of Intelligent Computing & Information Processing (Xiangtan University), Ministry of Education, Xiangtan, Hunan 411105); 4(School of Information Science & Engineering, Hunan University, Changsha 410082)
  • Online:2017-03-01

Abstract: In order to reduce the number of transmissions and balance the network load in wireless sensor network, this paper presents a data collection method by using hybrid compressive sensing (cs) in clustering network. Firstly we choose some nodes that are close to the temporary cluster-centroid as the candidate cluster head(CH), secondly determine the CH nodes on the basis of the distance of the candidate nodes to determined CH orderly. Then the common sensor nodes join their nearest cluster. Lastly we build a data transmission tree root of sink node that connects to all CHs greedy. When the number of data transmissions is higher than the threshold, nodes transmit data by using CS. On scenarios of compressive ratio equals 10,the simulation results demonstrate that the number of transmissions for the proposed method is 75% and 65% less than that of Clustering without CS and SPT without CS, 35% and 20% less than that of SPT with Hybrid CS and Clustering with Hybrid CS; The standard deviation of nodes transmissions for the proposed method is 62% and 81% less than that of Clustering without CS and SPT without CS, 41% and 19% less than that of SPT with Hybrid CS and Clustering with Hybrid CS.

Key words: wireless sensor network (WSN), compressive sensing, data collection, clustering network, load balance

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