Redundancy evaluation method of massive heterogeneous data in Internet of Things based on attributes and relations

Issue title: Big Data Networking-Challenges and Applications

Guest editors: Zhixiang Hou and Chong Shen

Article type: Research Article

Authors: Li, Ying^{; *}

Affiliations: School of Information Engineering, Jiaozuo University, Jiaozuo 454000, China

Correspondence: [*] Corresponding author. E-mail: yingliyy@21cn.com.

Abstract: In order to overcome the inaccuracy recognition results of the current network redundancy data, a new method based on attributes and relations for evaluating the redundancy of massive heterogeneous data in the Internet of Things is proposed. This method constructs a global database system and preliminarily obtains redundant data. Using angle variance, anomaly recognition and detection of Internet of Things data are carried out. A weighted network based on attribute distance is constructed, in which attributes and relations are used to calculate comprehensive redundancy, and heterogeneous data redundancy evaluation of the Internet of Things is realized. The experimental results show that the redundancy calculation accuracy and recall rate of the results of this method are both above 95%, which is obviously higher than other methods. It proves that the proposed method has high accuracy and strong anomaly recognition performance, and the integrated database of the Internet of Things is security.

Keywords: Internet of Things, heterogeneous data, redundancy, evaluation

DOI: 10.3233/WEB-200438

Journal: Web Intelligence, vol. 18, no. 2, pp. 167-177, 2020