王珂, 关慧珍, 张利利, 晁怡, 张源奔, 丁轩, 胡楚丽. 连续立体空间下传感器节点覆盖部署方法研‍究:以水质监测为例[J]. 武汉大学学报 ( 信息科学版), 2024, 49(2): 271-279. DOI: 10.13203/j.whugis20210325
引用本文: 王珂, 关慧珍, 张利利, 晁怡, 张源奔, 丁轩, 胡楚丽. 连续立体空间下传感器节点覆盖部署方法研‍究:以水质监测为例[J]. 武汉大学学报 ( 信息科学版), 2024, 49(2): 271-279. DOI: 10.13203/j.whugis20210325

连续立体空间下传感器节点覆盖部署方法研‍究:以水质监测为例

  • 摘要: 感知覆盖和网络通信对传感器网络监测的准确性、全面性以及数据传输具有重要意义。针对连续立体空间中传感器节点部署问题,提出了一种基于三维有限控制集的节点部署方法。首先采用一定的离散化尺寸将连续立体空间离散化,提取出一组可以代表连续空间中无限侯选位置的三维有限控制集;然后基于三维有限控制集构建考虑通信的最大覆盖模型。水下空间是一种典型的连续立体空间,一些水质监测变量在水下空间存在垂直方向的差异性,以水质监测为例,进行水下节点部署仿真,对空间离散化造成的误差进行分析,对比该方法和其他方法的覆盖效果。结果表明,提出的立体空间中节点部署方法可以有效部署节点,利用较少的节点达到较高的覆盖度,并且在部署节点数目较少的时候,仍可以保证节点之间的通信。

     

    Abstract:
    Objectives Coverage and communication are of great significance to the accuracy, comprehensiveness and data transmission of sensor network monitoring, especially in the case of different vertical monitoring requirements, traditional monitoring methods are difficult to achieve good coverage effect. We proposed a node coverage deployment method based on 3D finite dominating sets to solve the above problem.
    Methods The node deployment problem in continuous space is transformed into discrete maximum coverage location problem by 3D finite dominating sets. First, the continuous space is discretized by cubes, each cube is weighted according to the actual monitoring needs. A set of 3D finite dominating sets which can represent the infinite candidate positions in the continuous space is extracted. Then, a maximum coverage model considering communication is constructed to get the optimal deployment location of the sensors. Take water quality testing as an example, the underwater sensors deployment simulation is carried out. The communication effect between sensors and the influence of discrete size on the result are analyzed, and the coverage of this method compared with other methods was elucidated.
    Results The results show that, the sensor deployment method proposed in this study can effectively improve the coverage in the continuous three-dimensional space, achieve higher coverage through fewer nodes, and ensure the communication between sensors even if there are few deployed sensors. In addition, when the discretization size is small, the solution time is long, and the error between the model coverage and the actual coverage is small. On the contrary, when the discretization scale is large, the solving efficiency is high, but the error is relatively large.
    Conclusions The proposed method can effectively solve the problem of sensor deployment in three-dimensional space and efficiently obtain the data related to the monitored elements with different spatial distribution in vertical direction.

     

/

返回文章
返回