Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings

Research Article

SDN Based Content-Centric QoS-Guaranteed for Wireless Multimedia Sensor Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-74176-5_4,
        author={Gaolei Li and Jun Wu and Jianhua Li and Kuan Wang and Shan He},
        title={SDN Based Content-Centric QoS-Guaranteed for Wireless Multimedia Sensor Networks},
        proceedings={Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings},
        proceedings_a={INISCOM},
        year={2018},
        month={1},
        keywords={Wireless multimedia sensor networks (WMSNs) Software-defined network (SDN) Content-centric QoS-guaranteed Popularity k-nearest neighbor (KNN)},
        doi={10.1007/978-3-319-74176-5_4}
    }
    
  • Gaolei Li
    Jun Wu
    Jianhua Li
    Kuan Wang
    Shan He
    Year: 2018
    SDN Based Content-Centric QoS-Guaranteed for Wireless Multimedia Sensor Networks
    INISCOM
    Springer
    DOI: 10.1007/978-3-319-74176-5_4
Gaolei Li1,*, Jun Wu1,*, Jianhua Li1,*, Kuan Wang1,*, Shan He1,*
  • 1: Shanghai Jiao Tong University
*Contact email: gaolei_li@sjtu.edu.cn, junwuhn@sjtu.edu.cn, lijh888@sjtu.edu.cn, wangkuanfyfy@sjtu.edu.cn, hszoe1995@sjtu.edu.cn

Abstract

As the deployment of wireless multimedia sensor networks (WMSNs) increases sharply, the control granularity of traditional quality of service (QoS) technologies present weak assistance to satisfy the various application requirements. In this paper, we propose a novel architecture of software-defined network (SDN) based content-centric QoS-guaranteed (SCQG) for WMSNs. The SDN is an innovative network paradigm, which has capability to uniformly monitor and control the WMSNs’ infrastructures automatically. In proposed architecture, we formulate proper priority for each content based on its popularity in WMSNs. And also, we extend the SDN controller to match the content and make flow tables each content requests. Besides, we design an k-nearest neighbor (KNN) based machine-learning algorithm to identify the popularity of different contents.