Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers

Research Article

Support Vector Machine Based Range-Free Localization Algorithm in Wireless Sensor Network

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  • @INPROCEEDINGS{10.1007/978-3-319-52730-7_15,
        author={Tao Tang and Haicheng Liu and Haiyan Song and Bao Peng},
        title={Support Vector Machine Based Range-Free Localization Algorithm in Wireless Sensor Network},
        proceedings={Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers},
        proceedings_a={MLICOM},
        year={2017},
        month={2},
        keywords={Wireless Sensor Networks Localization Range-free Support Vector Machine},
        doi={10.1007/978-3-319-52730-7_15}
    }
    
  • Tao Tang
    Haicheng Liu
    Haiyan Song
    Bao Peng
    Year: 2017
    Support Vector Machine Based Range-Free Localization Algorithm in Wireless Sensor Network
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-52730-7_15
Tao Tang1,*, Haicheng Liu1, Haiyan Song1, Bao Peng2
  • 1: Heilongjiang Institute of Technology
  • 2: Shenzhen Institute of Information Technology
*Contact email: Tt8854@126.com

Abstract

Localization method is critical issues in Wireless Sensor Network (WSN) system. The existing node localization algorithms, especially range-based algorithms, did not consider the distances measured error and this may result in severe location errors that degrade the WSN performance. In this paper, a new algorithm called Support Vector Machine based Range-free localization (RFSVM) algorithm in WSN is proposed. This algorithm introduced a new matrix called transmit matrix which maps the relationship between the hops and distance. And use the SVM model to estimate the position of unknown nodes. This algorithm does not need any addition hardware, and the experiments shows that it can lead to the localization accuracy character good enough.