Josang信任模型的物联网感知层安全数据融合方法

Secure data aggregating methods by means of Josang trust models for the sensing layer of the internet of things

物联网感知层通常涉及大量传感器节点的运用,具有节点资源有限、分布广泛、无人值守、数据冗余、攻击容易等特点,实施安全数据融合是其必然选择。为了保障物联网感知层数据融合结果的真实性与可靠性,建立了一种结合数据预处理与节点信誉度评价的安全数据融合模型,利用粗大误差理论将明显偏离正常数据(或真值)的异常数据予以识别和剔除,基于概率统计理论计算和更新节点信誉度,只允许来源于高信誉度的节点数据参与数据融合,以Josang信任模型形成对数据融合结果的评价。仿真实验结果表明,该模型不仅有助于确保物联网感知层数据融合结果真实性与可靠性,而且基于粗大误差的数据预处理方法可减少数据融合的计算量,降低对传感器节点资源的需求。

The sensing layer of internet of things（ Io T） usually involves a large number of sensor nodes,which is characterized by limited nodes resources,wide distribution,unmanned operation,data redundancy,easy attack,etc. Carrying out secure data aggregation is thus necessary for Io T sensing layer. In order to guarantee the authenticity and reliability of the results from data aggregation of Io T sensing layer,a secure data aggregating model combining with data preprocessing and node creditability evaluation is proposed. Firstly,the abnormal data obviously deviated from normal data（ or true value）are identified and eliminated by means of gross error theory. Then nodes creditability are calculated and updated by means of probability and statistics theory,and nothing but the data of nodes with high creditability is allowed to involve in data aggregation. Finally,the model gains an evaluation of the results of data aggregation by means of Josang trust model. The simulation experiment results show that the model not only helps to guarantee authenticity and reliability of data aggregation results from Io T sensing layer,but also can reduce the calculating overload of data aggregation and the demand for sensor node resources by means of data preprocessing method based on gross error theory.