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基于感知源的数据驱动信任评测模型
Alternative Titledata-driven trust evaluation model based on the perception source
田鹤1,2; 郭凯红1; 王彦超2; 赵海3; 邵士亮4
Department机器人学研究室
Source Publication控制理论与应用
ISSN1000-8152
2020
Pages1-9
Contribution Rank4
Funding Organization教育部社会科学规划基金(16YJA630014) ; 国家自然科学基金(71771110) ; 辽宁省自然科学基金计划重点项目(20180540102)
Keyword物联网 信任评测 数据融合 无线传感器网络
Abstract

为解决物联网数据源头的可靠问题,构建一种基于感知源的数据驱动信任评测模型。模型以监测模块为评测单元,由中继节点完成其所在监测模块内感知节点的信任评测,通过感知节点自身数据之间的关系实现直接信任的计算,利用监测模块内各邻居节点之间关系实现推荐信任的计算,再结合历史信任,输出感知节点的综合信任。同时与模型预设的可疑阈值和异常阈值进行对比,更新历史信任和信任列表,实现感知节点的异常检测,利用预警检测误差和失信检测误差对模型的检测效果进行评价,统计结果表明模型能够保持较低的平均误差。将信任机制引入到数据融合过程,用综合信任作为加权因子,从而提高了数据融合的准确度。最后,通过实验仿真对信任评测模型进行评价,结果表明引入信任评测模型后延长了节点开始死亡的时间,随着节点的更新迭代,失信节点越来越少,在一定程度上提高了节点的存活率,延长了网络的生命周期。

Other Abstract

In order to solve the problem of reliability of source data in IoT, a data-driven trust evaluation model based on the perceptual source is established. In the model, the monitoring module is used as the evaluation unit, the relay nodes completed the trust evaluation of the sensor nodes in the monitoring module, the direct trust calculation is realized by the relationship with the sensor nodes' data, the recommendation trust calculation is realized by the relationship in the neighbor nodes in the monitoring module, and then combined with the historical trust to output the comprehensive trust of sensor nodes. Meanwhile, compared with the suspected threshold and the abnormal threshold preset by the model, updated the historical trust and trust list to realize the abnormal detection of the sensor nodes. Using the alert detection error and the dishonest detection error to evaluate the detection effect on the model, the statistical results show that the model can maintain a low average error. The trust mechanism is introduced into the data fusion process; the comprehensive trust was used as the weighting factor, thus, the accuracy of the data fusion is improved. Finally, the trust evaluation model is evaluated by experiment simulation, the results show that it prolongs the time of node death after leading into the trust evaluation model. With the update iteration of nodes, the dishonest nodes will be fewer and fewer. It improves the nodes’ survival rate and prolongs the life cycle of the networks to a certain extent.

Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27659
Collection机器人学研究室
Corresponding Author郭凯红
Affiliation1.辽宁大学信息学院
2.辽宁科技学院曙光大数据学院
3.东北大学计算机科学与工程学院
4.中国科学院沈阳自动化研究所机器人学国家重点实验室
Recommended Citation
GB/T 7714
田鹤,郭凯红,王彦超,等. 基于感知源的数据驱动信任评测模型[J]. 控制理论与应用,2020:1-9.
APA 田鹤,郭凯红,王彦超,赵海,&邵士亮.(2020).基于感知源的数据驱动信任评测模型.控制理论与应用,1-9.
MLA 田鹤,et al."基于感知源的数据驱动信任评测模型".控制理论与应用 (2020):1-9.
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