无线传感网络测点数据共享中的差异数据分界  

Difference Data Boundary in Data Sharing of Wireless Sensor Network

作　　者：高原[1] GAO Yuan(Teacher Education Experimental Center,Tianjin Normal University,Tianjin 300387,China)

机构地区：[1]天津师范大学教师教育实验中心,天津300387

出　　处：《计算机仿真》2020年第10期244-248,共5页Computer Simulation

基　　金：天津师范大学教育科学研究基金项目(1353P2WT1606)。

摘　　要：传统方法分界准确性较差,实现过程较为复杂,分界效率低下,对差异性较大的数据可有效分界,但针对差异性小的数据无法准确分类,可行性较差。为此提出一种新的无线传感网络测点数据共享中差异数据分界方法。引入隐马尔科夫概率统计模型,选用一种快速计算的训练方法,将其应用于国家示范中心虚拟仿真环境下无线传感网络测点数据共享中差异数据分界训练中,为差异数据分界提供依据,实现信息化管理。通过训练后的隐马尔科夫概率统计模型,实现差异数据分界。实验结果表明,所提方法能够让无线传感网络测点数据共享中的差异数据有更好的可分界性,分界结果更加准确,且可有效节约存储空间,大大降低计算复杂度,对提高整体运行效率有很大的作用。The traditional method often leads to poor accuracy, complex realization process and low bounding efficiency. But this method only can divide the data with great difference not be classified accurately according to the data with small difference, and the feasibility is poor. In this article, a new method of dividing differential data in measure point data sharing of wireless sensor network was presented. Firstly, the hidden Markov probability statistical model was introduced and a training method with fast computing was selected to be applied to the training of differential data bounding in the measure point data sharing of wireless sensor networks under the virtual simulation environment of national demonstration center, so as to provide the basis for dividing the difference data and realize the information management. Thus, the hidden Markov probability statistical model after the training was used to realize the difference data bounding. Simulation results show that the proposed method can make the difference data in wireless sensor network data sharing have better boundedness, and the results are more accurate. Meanwhile, the storage space can be effectively saved and the computational complexity can be greatly reduced. Thus, this method plays a great role on improving the efficiency of the whole operation.

关 键 词：无线传感网络 测点数据 共享 差异数据 分界 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]