Real-Time Image Transmission Algorithm in WSN with Limited Bandwidth

Authors

  • Hui Huang College of technology, Hubei engineering university
  • Zhe Li School of Economics and Management, Hubei Engineering University

DOI:

https://doi.org/10.3991/ijoe.v14i12.9494

Keywords:

limited bandwidth networks, real-time image transmission, compression algo-rithm, synchronous network architecture, NVIDIA TX1

Abstract


In this paper, a real-time image transmission algorithm in WSN with limited bandwidth networks is studied. Firstly, a simple and effective monitoring network architecture is established, which allows multiple video monitoring nodes to access the network, and the data transmission is controlled by the synchronization mechanism without collision. Then, the image data is compressed locally at the monitoring nodes (over 85%), so that the image of each node can meet the needs of real-time data transmission, and the overall power consumption of the system is greatly reduced. Finally, based on NVIDIA TX1, four test nodes are constructed to test the algorithm cumulatively, which verifies the effectiveness of the system framework and compression algorithm.

Author Biographies

Hui Huang, College of technology, Hubei engineering university

Hui Huang is an Associate Professor of College of Technology, Huber Engineering University in China. His research interests include image process, embedded system, computer science and big data and data mining.

Zhe Li, School of Economics and Management, Hubei Engineering University

Zhe Li received the Ph.D. degree. He is currently a Lecturer with the School of Economics and Management, Hubei Engineering University. His research interests include the consistency control of multiple agents in complex environments, multi-rates with multi-agent systems, and distributed control systems.

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Published

2018-12-23

How to Cite

Huang, H., & Li, Z. (2018). Real-Time Image Transmission Algorithm in WSN with Limited Bandwidth. International Journal of Online and Biomedical Engineering (iJOE), 14(12), pp. 57–71. https://doi.org/10.3991/ijoe.v14i12.9494

Issue

Vol. 14 No. 12 (2018)

Section

Papers

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