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    Lin Weiwei, Yao Yingbiao, Zou Ke, Feng Wei, Yan Junrong. Correction Vector Based Distributed DV-Hop Localization Refinement Algorithm[J]. Journal of Computer Research and Development, 2019, 56(3): 585-593. DOI: 10.7544/issn1000-1239.2019.20170841
    Citation: Lin Weiwei, Yao Yingbiao, Zou Ke, Feng Wei, Yan Junrong. Correction Vector Based Distributed DV-Hop Localization Refinement Algorithm[J]. Journal of Computer Research and Development, 2019, 56(3): 585-593. DOI: 10.7544/issn1000-1239.2019.20170841

    Correction Vector Based Distributed DV-Hop Localization Refinement Algorithm

    • Node location technology is one of the hot topics in current wireless sensor networks(WSNs). The DV-Hop (distance vector hop) localization algorithm, based on the hop distance estimation, is a typical representation of range-free localization algorithm. The advantages of DV-Hop is simple and easy implementation, and its disadvantage is low positioning accuracy which is resulting from the hop-distance ambiguity problem. Focusing on the hop-distance ambiguity problem of the traditional DV-Hop localization algorithm, this paper proposes a correction vector based distributed localization refinement algorithm (CVLR). Firstly, based on the localization results of DV-Hop, CVLR constructs the position correction vector using the pseudo ranging distance and the positioning distance between neighbors and unknown nodes. Secondly, the refinement process is modeled to minimize the square sum of the difference between the two distances in the direction of correction vector. Finally, a simple iterative search method is proposed to solve above minimization problem. In practice, CVLR consists of CVLR1 and CVLR2. CVLR1 can make full use of the information of 1-hop neighbors, and CVLR2 can make full use of the information of 1-hop and 2-hop neighbors. The simulation results show that, compared with DV-Hop, DV-RND (an improved DV-Hop localization algorithm based on regulated neighborhood distance), and DV-EA (an improved DV-Hop localization algorithm based on evolutionary algorithm), CVLR1 improves the positioning accuracy by about 30%, 25%, and 20%, and CVLR2 improves the positioning accuracy by about 45%, 42%, and 40%, on average.
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