Aggregation Tree Based Data Aggregation Algorithm in Wireless Sensor Networks

Authors

  • Hu Yanhua Department of Electrical and Computer Engineering, Lushan College of Guangxi University of Science and Technology
  • Xincai Zhang Department of Information Technology, College of Information & Business, Zhong yuan University of Technology, Zhengzhou, Henan 451191, China

DOI:

https://doi.org/10.3991/ijoe.v12i06.5408

Keywords:

Abstract, In wireless sensor networks, efficient and effective data aggregation algorithms can prolong the network lifecycle by reducing communication of redundant data and improve the security of the networks. Tradition data aggregation algorithms in wir

Abstract


Abstract: In wireless sensor networks, efficient and effective data aggregation algorithms can prolong the network lifecycle by reducing communication of redundant data and improve the security of the networks. Tradition data aggregation algorithms in wireless sensor networks mainly aim to improve the energy utilization, and ignore the security and lifecycle. In order to get a good trade-off between these requirements, we proposed a data aggregation algorithm based on constructing a data aggregation tree. After give a formalism description of the problem, we proposed a data aggregation tree constructing algorithm. By minimize the maximal energy consumption of nodes, the algorithm can prolong the lifecycle. In data aggregation scheduling algorithm, we select the number of communications carefully to get the trade-off between low weighted delay and high network lifecycle. The simulation experiments show that, the proposed data aggregation algorithm consumes less energy while aggregating data from sensor nodes, and thus can prolong the network lifecycle.

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Published

2016-06-30

How to Cite

Yanhua, H., & Zhang, X. (2016). Aggregation Tree Based Data Aggregation Algorithm in Wireless Sensor Networks. International Journal of Online and Biomedical Engineering (iJOE), 12(06), pp. 10–15. https://doi.org/10.3991/ijoe.v12i06.5408

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Papers