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Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings

Research Article

An Adaptive and Efficient Network Traffic Measurement Method Based on SDN in IoT

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  • @INPROCEEDINGS{10.1007/978-3-030-97124-3_6,
        author={Wansheng Cai and Xi Song and Chuan Liu and Dingde Jiang and Liuwei Huo},
        title={An Adaptive and Efficient Network Traffic Measurement Method Based on SDN in IoT},
        proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings},
        proceedings_a={SIMUTOOLS},
        year={2022},
        month={3},
        keywords={Internet of Things Software Defined Network Optimization algorithm Network measurement},
        doi={10.1007/978-3-030-97124-3_6}
    }
    
  • Wansheng Cai
    Xi Song
    Chuan Liu
    Dingde Jiang
    Liuwei Huo
    Year: 2022
    An Adaptive and Efficient Network Traffic Measurement Method Based on SDN in IoT
    SIMUTOOLS
    Springer
    DOI: 10.1007/978-3-030-97124-3_6
Wansheng Cai1, Xi Song2, Chuan Liu3, Dingde Jiang4, Liuwei Huo4
  • 1: State Grid Electric Power Research Institute
  • 2: State Grid Gansu Electric Power CORP.
  • 3: Global Energy Interconnection Research Institute Co., Ltd.
  • 4: School of Astronautics and Aeronautics, University of Electronic Science and Technology of China

Abstract

The Internet of Things (IoT) is a worldwide information network that connects thousands of technological gadgets. We incorporate the SDN network architecture into IoT networks and investigate the characteristics of SDN-based IoT networks in order to make the IoT more flexible and extendable. SDN (Software Defined Networking) is a logical control center with a centralized control plane that makes network management more flexible and efficient. For IoT network management, fine-grained and reliable traffic information is critical. Then, in SDN-based IoT networks, we construct a network traffic model by analyzing the self-similarity of network traffic in IoT network. Then, we collect some traffic statistics in OpenFlow-based switches as the source data and use it to train the proposed network traffic estimation model. Using the measured network traffic in the IoT network, we use the Kalman Filtering to measure and estimate each flow, this scheme just increases a little overhead. Then, we propose to an algorithm to search the more accuracy of traffic. Finally, we run additional simulations to ensure that the suggested measuring system is accurate. Simulation findings suggest that using intelligent optimization approaches, we can improve the granularity and accuracy of traffic data.

Keywords
Internet of Things Software Defined Network Optimization algorithm Network measurement
Published
2022-03-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-97124-3_6
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