面向物联网的用户感知数据去重监控方法仿真
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摘要
对物联网用户感知数据进行去重监控,能够有效提升物联网服务质量。当前物联网用户数据量庞大,且对其进行去重处理时忽略了对数据进行动态权重赋值,导致物联网用户感知数据去重监控精度偏低。针对此问题,提出基于数据观测器的物联网用户感知数据去重监控方法。利用数据观测器对数据监控外扰进行计算,获得纯滞后的闭环函数,通过闭环函数得到控制器整定参数。根据整定结果建立数据监控模型,并对该模型的输出进行补偿。利用逐级聚类方法(SCDE),通过关键属性分割将物联网数据划分成小数据集,然后精确检测相似重复记录,结合基于动态权重的模糊实体匹配策略,对数据进行动态权重赋值,降低数据重复存储带来的影响,以此提高了数据去重监控过程精度。实验结果显示所提方法在响应时间和监控精度上均优于传统算法。
The monitoring and deduplication for perception data of user in Internet of things can effectively improve the service quality of Internet of Things. Currently, the dynamic weight assignment of data during deduplication is ignored, leading to low accuracy of data deduplication. Therefore, this paper presents a method of monitoring and deduplication for user perception data in Internet of Things based on data observer. Firstly, the data observer was used to calculate the external disturbance of data monitoring and obtain the pure time-delay closed-loop function. Then, the closed-loop function was used to get tuning parameter of controller. According to the tuning result, the data monitoring model was built and the output of this model was compensated. SCDE was used to divide data in the Internet of Things into small record sets based on key attribute segmentation. After that, similar duplicate records were accurately detected. Combined with fuzzy entity matching method based on dynamic weight, dynamic weight assignment of data was carried out. Finally, the influence of repeated storage was reduced. Thus, the accuracy of data deduplication monitoring was increased. Simulation results prove that the proposed method is superior to the traditional algorithm in response time and monitoring accuracy.
The monitoring and deduplication for perception data of user in Internet of things can effectively improve the service quality of Internet of Things. Currently, the dynamic weight assignment of data during deduplication is ignored, leading to low accuracy of data deduplication. Therefore, this paper presents a method of monitoring and deduplication for user perception data in Internet of Things based on data observer. Firstly, the data observer was used to calculate the external disturbance of data monitoring and obtain the pure time-delay closed-loop function. Then, the closed-loop function was used to get tuning parameter of controller. According to the tuning result, the data monitoring model was built and the output of this model was compensated. SCDE was used to divide data in the Internet of Things into small record sets based on key attribute segmentation. After that, similar duplicate records were accurately detected. Combined with fuzzy entity matching method based on dynamic weight, dynamic weight assignment of data was carried out. Finally, the influence of repeated storage was reduced. Thus, the accuracy of data deduplication monitoring was increased. Simulation results prove that the proposed method is superior to the traditional algorithm in response time and monitoring accuracy.
引文
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[2] 王宏远,祝烈煌,李龙一佳. 云存储中支持数据去重的群组数据持有性证明[J]. 软件学报, 2016,27(6):1417-1431.
[3] 周玉坤,等. 面向数据去重的基于二次哈希的收敛加密策略[J]. 计算机工程与科学, 2016,38(9):1755-1762.
[4] 罗恩韬,王国军, 李超良. 大数据环境中多维数据去重的聚类算法研究[J]. 小型微型计算机系统, 2016,37(3):438-442.
[5] 刘竹松,何喆. 基于Merkle哈希树的云存储加密数据去重复研究[J]. 计算机工程与应用, 2018,(5):85-90.
[6] 陆浩,卢军,修榕康. 密码字典数据去重算法研究[J]. 软件导刊, 2017,(2):57-59.
[7] 姚敏,等. 基于数据路由的分布式备份数据去重系统[J]. 计算机工程, 2017,43(2): 85-91.
[8] 罗恩韬,王国军,李超良. 大数据环境中多维数据去重的聚类算法研究[J]. 小型微型计算机系统, 2016,37(3):438-442.
[9] 夏容,邹小花. 分布式数据库中数据存取效率管理仿真[J]. 计算机仿真, 2017,34(10): 392-395.
[10] 郝朝鹏. 数据备份系统中数据去重技术分析[J]. 军民两用技术与产品, 2017,(10): 52-53.