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Network coding based converge-cast scheme in wireless sensor networks

Xu, Li, Zhang, Jing, Xiang, Yang and Huang, Xinyi 2016, Network coding based converge-cast scheme in wireless sensor networks, Wireless personal communications, In Press, pp. 1-26, doi: 10.1007/s11277-016-3725-7.

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Title Network coding based converge-cast scheme in wireless sensor networks
Author(s) Xu, Li
Zhang, Jing
Xiang, Yang
Huang, Xinyi
Journal name Wireless personal communications
Season In Press
Start page 1
End page 26
Total pages 26
Publisher Springer
Place of publication Berlin, Germany
Publication date 2016-09-28
ISSN 0929-6212
Keyword(s) converge-cast
network coding
rate prediction
connected dominating set
wireless sensor networks
Summary 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 To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs.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.
Language eng
DOI 10.1007/s11277-016-3725-7
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer
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Document type: Journal Article
Collection: School of Information Technology
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