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Wireless Networks

, Volume 25, Issue 4, pp 1875–1893 | Cite as

Minimization of delay and collision with cross cube spanning tree in wireless sensor networks

  • Jing ZhangEmail author
  • Li Xu
  • Pei-Wei Tsai
  • Zhiwei Lin
Article
  • 208 Downloads

Abstract

The wireless sensor network (WSN) is a system containing the event detection and the data gathering abilities. The data gathering mechanism is the fundamental but important procedure in the WSN environment. The way of the data gathering majorly affects the efficiency of WSNs on retrieving data at the sink node. It is generally known that the clustering techniques are effective to reduce the energy consumption in the WSNs. However, the research on the packet collision and the transmission delay in the Cluster based routing algorithm still remains limited. The packet loss and the transmission delay will happen more often due to collision and as such it will have negative impact on the WSN performance. In addition, the transmission delay phenomenon in the WSN may cause the inefficient result in the data gathering process. Unfortunately, it is usually neglected in the existing literature. To overcome the drawback of transmission delay and collision, a cluster-based converge cast tree (CCCT) protocol is proposed in this paper. The core of this protocol is to construct a cross cube spanning tree topology control algorithm. The proposed protocol performance is analyzed theoretically, which demonstrate that the protocol is efficient in avoiding packet collision and reducing the transmission delay. Finally, the protocol is examined by the simulations. The simulation results indicate that the proposed CCCT structure and algorithms outperform the existing approaches significantly in the realistic WSN environment.

Keywords

Wireless sensor networks Connected documenting set Collision Delay Spanning tree 

Notes

Acknowledgements

The authors wish to thank National Natural Science Foundation of China (Grant Nos.: 61072080, 61572010), Natural Science Foundation of Fujian Province of China (2017J05098), The Education Department of Fujian Province Science and Technology Project (JAT160328, JZ160461), and the Science Research Project in Fujian University of Technology (GY-Z160066, GY-Z160130, GY-Z160138).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringFujian University of Technology, and Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsFuzhouChina
  2. 2.School of Mathematics and Computer ScienceFujian Normal UniversityFuzhouChina
  3. 3.Department of Computer Science and Software EngineeringSwinburne University of TechnologyHawthornAustralia
  4. 4.School of ComputingUlster UniversityJordanstownNorthern Ireland, UK

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