Outlier Detection Techniques for Wireless Sensor Networks: A Survey

TLDR

In wireless sensor networks, outliers are data points that deviate significantly from normal patterns, arising from noise, events, or malicious attacks, and traditional detection methods are unsuitable due to the networks’ unique data characteristics and constraints. This survey offers a comprehensive review of outlier detection techniques tailored for wireless sensor networks. It introduces a taxonomy and comparative table that help users choose appropriate techniques based on data type, outlier type, identity, and degree.

Abstract

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree.