摘要
为了解决传统传感器连通算法难以完全剔除连通干扰因子,导致节点连通性较差,提出将灰色关联分析方法应用在传感器节点连通算法设计中。采集传感器节点信息,确定各个节点之间的灰度关联关系,通过序列因子之间的极差转化剔除序列中的连通干扰因子。采用K邻近搜索算法获得特征点附近邻域点,并凭借特征向量与特征值建造局部节点连通范围平面。最后提取节点连通点与线的特征,利用矩形节点连通区域进行分割,从而实现传感器节点的高效连通。仿真分析结果表明,与传统连通算法相比,提出的算法能够降低节点跳数,并能够保持较高连通率与连通稳定性。因此,说明提出的算法能够全方面提高节点连通性能。
In order to solve the problem that the traditional sensor connectivity algorithm is difficult to completely eliminate the connectivity interference factors, resulting in poor node connectivity, the grey correlation analysis method is applied to the design of sensor node connectivity algorithm. Collect the sensor node information, determine the gray correlation between each node, and eliminate the connected interference factors in the sequence through the range transformation between the sequence factors. The K-nearest neighbor search algorithm is used to obtain the neighborhood points near the feature points, and the local node connected range plane is constructed by using the feature vector and eigenvalue. Finally, the features of node connected points and lines are extracted, and the rectangular node connected region is used to segment, so as to realize the efficient connection of sensor nodes. Simulation results show that compared with the traditional connectivity algorithm, the proposed algorithm can reduce the number of node hops and maintain high connectivity and connectivity stability. Therefore, it shows that the proposed algorithm can improve the node connectivity performance in all aspects.
作者
张苏颖
竺兴妹
许曙青
ZHANG Suying;ZHU Xingmei;XU Shuqing(Department of Electronic Engineering,Jiangsu Union Technical Institute Nanjing Engineering branch,Nanjing Jiangsu 210035,China;College of Economics and Management,China University of Mining and Technology,Xuzhou Jiangsu 221116,China)
出处
《传感技术学报》
CAS
CSCD
北大核心
2022年第1期127-131,共5页
Chinese Journal of Sensors and Actuators
基金
江苏省教育厅项目(BRA2016432)。
关键词
传感器节点
连通算法
灰色关联分析
K邻近搜索算法
干扰因子
sensor nodes
connectivity algorithm
grey correlation analysis
k-nearest neighbor search algorithm
interference factor
