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

Research Article

Coverage Improvement Strategy Based on Voronoi for Directional Sensor Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-52730-7_25,
        author={Shan You and Guanglin Zhang and Demin Li},
        title={Coverage Improvement Strategy Based on Voronoi for Directional Sensor Networks },
        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={Coverage Voronoi DSNs Sensor network},
        doi={10.1007/978-3-319-52730-7_25}
    }
    
  • Shan You
    Guanglin Zhang
    Demin Li
    Year: 2017
    Coverage Improvement Strategy Based on Voronoi for Directional Sensor Networks
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-52730-7_25
Shan You,*, Guanglin Zhang,*, Demin Li,*
    *Contact email: shanyou@mail.dhu.edu.cn, glzhang@dhu.edu.cn, deminli@dhu.edu.cn

    Abstract

    Nowadays, directional sensor networks (DSNs) have drawn a lot of attentions, which are made up of a large number of tiny directional sensors that are different from traditional omnidirectional sensors. Directional sensor is characterized by working direction and angle of view (AOV). In this paper we study area coverage of DSNs. We exploit Voronoi theory to divide sensors into polygons, by optimizing the local coverage in each polygon to achieve the overall coverage. We take full use of Voronoi vertexes and edges to judge whether a sensor gets full coverage inside in current polygon, if not, then the sensor calls Move Inside Cell Algorithm (MIC) and Rotate Working Direction Algorithm (RWD) algorithms we have designed. Compared to the similar methods to solve this question our algorithms are relatively simple and moving distance is shorter. Simulation results reveal that our algorithms outperform some existing methods in term of the area coverage.