sesa 15(6): e2

Research Article

A Hierarchical of Security Situation Element Acquisition Mechanism in Wireless Sensor Network

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  • @ARTICLE{10.4108/icst.mobimedia.2015.259030,
        author={Li Fangwei and Wang Yan and Zhu Jiang and Zhang Xinyue},
        title={A Hierarchical of Security Situation Element Acquisition Mechanism in Wireless Sensor Network},
        journal={EAI Endorsed Transactions on Security and Safety},
        volume={2},
        number={6},
        publisher={EAI},
        journal_a={SESA},
        year={2015},
        month={8},
        keywords={network security, situational factors, svm, nmf},
        doi={10.4108/icst.mobimedia.2015.259030}
    }
    
  • Li Fangwei
    Wang Yan
    Zhu Jiang
    Zhang Xinyue
    Year: 2015
    A Hierarchical of Security Situation Element Acquisition Mechanism in Wireless Sensor Network
    SESA
    EAI
    DOI: 10.4108/icst.mobimedia.2015.259030
Li Fangwei1, Wang Yan1,*, Zhu Jiang1, Zhang Xinyue1
  • 1: Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications
*Contact email: wangyan2250@sina.com

Abstract

In wireless sensor network, the processing ability of the sensor nodes is poor. And the security situational element acquisition is also a serious problem. Thus, this paper proposes a hierarchical framework of security situational elements acquisition mechanism. In this framework, support vector machine hyper sphere multi class algorithm is introduced as basic classifier. The method of attribute reduction uses non negative matrix factorization algorithm. The fuzzy classification algorithm used to initialize non negative matrix factorization, in order to avoid the local optimal which is caused by non negative matrix factorization random initialization. In the sink node classification rules and attribute reduction rules are formed by learning. The classification analyses respectively focus on the cluster head and sink node, which can reduce the requirement of the sensor node properties. Attribute reduction before the data transmission, which reduces communication consumption data transmission, improves the performance of classifiers. By simulation analysis, the scheme has preferably accuracy in the situation elements acquisiton, and smaller communication overhead in the process of information transmission.