【文件属性】：
文件名称：Characteristics and classification of outlier detection
文件大小：822KB
文件格式：PDF
更新时间：2017-01-30 09:36:53
网络异常检测
Wireless sensor networks (WSNs) have received considerable attention for multiple
types of applications. In particular, outlier detection in WSNs has been an area of vast
interest. Outlier detection becomes even more important for the applications involving harsh
environments, however, it has not received extensive treatment in the literature. The identification
of outliers in WSNs can be used for filtration of false data, find faulty nodes and
discover events of interest. This paper presents a survey of the essential characteristics for the
analysis of outlier detection techniques in harsh environments. These characteristics include,
input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier
types(local and global), type of approach(distributed/centralized), outlier identification(event
or error), outlier degree, outlier score, susceptibility to dynamic topology, non-stationarity
and inhomogeneity.Moreover, the prioritization of various characteristics has been discussed
for outlier detection techniques in harsh environments. The paper also gives a brief overview
of the classification strategies for outlier detection techniques in WSNs and discusses the
feasibility of various types of techniques for WSNs deployed in harsh environments.