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Wireless Personal Communications

, Volume 96, Issue 4, pp 4947–4972 | Cite as

Network Coding Based Converge-Cast Scheme in Wireless Sensor Networks

  • Li Xu
  • Jing ZhangEmail author
  • Yang Xiang
  • Xinyi Huang
Article

Abstract

The converge-cast in wireless sensor networks (WSNs) is widely applied in many fields such as medical applications and the environmental monitoring. WSNs expect not only providing routing with high throughput but also achieving efficient energy saving. Network coding is one of the most promising techniques to reduce the energy consumption. By maximizing the encoding number, the message capacity per package can be extended to the most efficient condition. Thus, many researchers have focused their work on this field. Nevertheless, the packages sent by the outer nodes need to be temporary stored and delayed in order to maximize the encoding number. To find out the balance between inserting the delay time and maximizing the encoding number, a Converge-cast Scheme based on data collection rate prediction (CSRP) is proposed in this paper. To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs. Furthermore, a novel coding conditions based on CDS is proposed to increase the coding opportunity and to solve the collision problems. The corresponding analysis and experimental results indicate that the feasibility and efficiency of the CSRP is better than normal conditions without the prediction.

Keywords

Converge-cast Network coding Rate prediction Connected dominating set Wireless sensor networks 

Notes

Acknowledgments

The authors wish to thank National Natural Science Foundation of China (Grant Nos.: 61072080, 61572010). National Natural Science Foundation of China (Nos. 61072080, U1405255). The Education Department of Fujian Province science and technology project (JAT160328, JA14217, JA15329), the scientific research project in Fujian University of Technology (GY-Z160066) Fujian Normal University Innovative Research Team (No. IRTL1207), Fujian provincial key project of science and technology (2015H0009, 2014H0008, 2014J01218).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Mathematics and Computer ScienceFujian Normal UniversityFuzhouPeople’s Republic of China
  2. 2.School of Information Science and Engineering, Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouPeople’s Republic of China
  3. 3.School of Information TechnologyDeakin UniversityMelbourneAustralia

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