A Methodology for Reliability of WSN Based on Software Defined Network in Adaptive Industrial Environment

Ying Duan; Wenfeng Li; Xiuwen Fu; Yun Luo; Lin Yang

doi:10.1109/JAS.2017.7510751

Volume 5 Issue 1

Jan. 2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(1): 74-82

Ying Duan, Wenfeng Li, Xiuwen Fu, Yun Luo and Lin Yang, "A Methodology for Reliability of WSN Based on Software Defined Network in Adaptive Industrial Environment," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 74-82, Jan. 2018. doi: 10.1109/JAS.2017.7510751

Citation:

Ying Duan, Wenfeng Li, Xiuwen Fu, Yun Luo and Lin Yang, "A Methodology for Reliability of WSN Based on Software Defined Network in Adaptive Industrial Environment," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 74-82, Jan. 2018. doi: 10.1109/JAS.2017.7510751

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A Methodology for Reliability of WSN Based on Software Defined Network in Adaptive Industrial Environment

doi: 10.1109/JAS.2017.7510751

Ying Duan^{1, 2
,},
Wenfeng Li^{1
,
,},
Xiuwen Fu^3
,,
Yun Luo^1
,,
Lin Yang^1
,

1.
School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
2.
Department of Computer Science and Application, Zhengzhou University of Aeronautics, Zhengzhou 450015, China
3.
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

Funds:

the National Natural Science Foundation of China 61571336

the Science and Technology Project of Henan Province in China 172102210081

the Independent Innovation Research Foundation of Wuhan University of Technology 2016-JL-036

More Information

Author Bio:
Ying Duan is a Ph.D. candidate at Wuhan University of Technology under the supervision of Dr. Wenfeng Li. She received the M.S. degree in computer science and technology from Wuhan University of Technology, China. Her research interests include industrial internet of things, including the wireless sensor network, opportunistic networks, software defined network(e-mail: able0607@163.com)

Wenfeng Li (M'01-SM'12) is a Full Professor at the School of Logistics Engineering, Wuhan University of Technology. He set up the Institute of Logistics and Robotics, the Center of IoT and Logistics Technologies, and the International Joint Laboratory of Internet of Things. He graduated in mechanical engineering at Zhengzhou Light Industry Institute, and received the master degree from Huazhong University of Science and Technology and the Ph.D. degree from Wuhan University of Technology. He is a Senior Member of both IEEE and China Society of Logistics, TC member of IEEE CSCWD, a committee member of Teaching Guiding Committee for Logistics Education of Ministry of Education of China, a member of Intelligent Manufacture Committee of China. He has contributed to IEEE as a Session Co-Chair of IEEE SMC 2009-2013, a Program Committee Co-Chair of the International Working Group on CSCW in Design (2012) and a Program Committee Co-Chair of the International Conference of ICMA 2010 and ICIA 2010. His research interests include internet of things, swarm robots, logistics and supply chain, and complex systems(e-mail: liwf@whut.edu.cn)

Xiuwen Fu is a Lecturer at the Institute of Logistics Science and Engineering, Shanghai Maritime University. He received the Ph.D. degree at Wuhan University of Technology. His research interests include wireless sensor networks, opportunistic networks, and industrial internet of things(e-mail: fuxiuwen1987@163.com)

Yun Luo received the B.S. degree from the Department of Computer Science and Technology, Wuhan University of Technology in Wuhan, China. He is currently working towards the Ph.D. degree in the Department of Logistics Engineering, Wuhan University of Technology. His research interests include sensor networks, multi-mobile robots, and software defined network(e-mail: yunluo@whut.edu.cn)

Lin Yang is a Ph.D. candidate at Wuhan University of Technology under the supervision of Dr. Wenfeng Li. He received the B.S. degree in computer science and technology from Navy University of Engineering, China. His research interests include peoplecentric internet of things, including the mobility model, connectivity and network protocols(e-mail: lyang@whut.edu.cn)
Corresponding author: Wenfeng Li, e-mail:liwf@whut.edu.cn
Received Date: 2016-05-31
Accepted Date: 2017-04-21

Abstract

Abstract

As communication technology and smart manufacturing have developed, the industrial internet of things (IIoT) has gained considerable attention from academia and industry. Wireless sensor networks (WSNs) have many advantages with broad applications in many areas including environmental monitoring, which makes it a very important part of IIoT. However, energy depletion and hardware malfunctions can lead to node failures in WSNs. The industrial environment can also impact the wireless channel transmission, leading to network reliability problems, even with tightly coupled control and data planes in traditional networks, which obviously also enhances network management cost and complexity. In this paper, we introduce a new software defined network (SDN), and modify this network to propose a framework called the improved software defined wireless sensor network (improved SD-WSN). This proposed framework can address the following issues. 1) For a large scale heterogeneous network, it solves the problem of network management and smooth merging of a WSN into IIoT. 2) The network coverage problem is solved which improves the network reliability. 3) The framework addresses node failure due to various problems, particularly related to energy consumption. Therefore, it is necessary to improve the reliability of wireless sensor networks, by developing certain schemes to reduce energy consumption and the delay time of network nodes under IIoT conditions. Experiments have shown that the improved approach significantly reduces the energy consumption of nodes and the delay time, thus improving the reliability of WSN.
- Industrial internet of things (IIoT),
- reliability,
- software defined network (SDN),
- wireless sensor network (WSN)

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References(33)

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A Methodology for Reliability of WSN Based on Software Defined Network in Adaptive Industrial Environment

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