文献类型：期刊文献

英文题名：Anomaly Detection Approach for Urban Sensing Based on Credibility and Time-Series Analysis Optimization Model

作者：Zhang, Hong[1,2];Li, Zhanming[1]

第一作者：张红;Zhang, Hong

通信作者：Zhang, H[1];Zhang, H[2]

机构：[1]Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China;[2]Gansu Univ Chinese Med, Networks & Informat Adm Ctr, Lanzhou 730000, Gansu, Peoples R China

第一机构：Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China;[2]corresponding author), Gansu Univ Chinese Med, Networks & Informat Adm Ctr, Lanzhou 730000, Gansu, Peoples R China.|[10735]甘肃中医药大学;

年份：2019

卷号：7

起止页码：49102

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20191906880327);Scopus(收录号：2-s2.0-85065092625);WOS:【SCI-EXPANDED(收录号:WOS:000466704500001)】；

基金：This work was supported in part by the Project of Gansu Province for Industrial and Information Development under Grant 23051358, and in part by the Project of Gansu Province for Guiding Scientific and Technological Innovation and Development under Grant 2018ZX-05.

语种：英文

外文关键词：Anomaly detection; urban sensing; credibility; spatio-temporal correlation; smart city

摘要：Urban sensor networks often consist of a large number of low-cost sensor nodes. Due to the constrained resource devices and hazardous deployment, urban sensing is vulnerable to interference and destruction of external factors or the impact of external environmental emergencies. Abnormal data, outliers, or anomalies have affected the utility in various domains seriously. Timely and accurate detection of unexpected events, monitoring of network performance, and anomaly detection of data flow are of great significance to improve the decision-making ability of the system. In this paper, we propose an anomaly detection method for urban sensing based on sequential data and credibility. First, based on Bayesian methods, a reputation model is established for the selection of credible sample points. Second, aiming at the problem that the threshold range is difficult to determine in the traditional method, the pivot quantity is defined by using the median of the credible sample, and the confidence interval can be estimated to quantify the deviation degree of the sensor data. Finally, an anomaly data identification and source verification approach is proposed to distinguish errors and events accurately. The evaluation results on both the detection rate and the false positive rate demonstrate a better performance of our approach than the other existing methods.