A Literature Review on Outlier Detection in Wireless Sensor Networks

Abstract

Wireless sensor networks have become an important element of technologies such as the Internet ofThings due to their ability to obtain sensory data from the physical world in tracking and monitoring applications. However, such networks are susceptible to the presence of outliers mainly due to errors or failures in the sensor nodes or the presence of events that alter the reading patterns. To address this problem, many researchers have turned their efforts to the development of outlier detection techniques that achieve maximum detection rate with the highest possible efficiency, given the limited resources typical of this type of networks. In this study, 33 papers on outlier detection techniques in wireless sensor networks between 2018 and 2023 were analyzed with the aim of describing the characteristics of these techniques, their metrics and test conditions, application areas, and possible limitations. The results showed mostly hybrid, distributed, online and multivariate sensing proposals in addition to the exploitation of spatiotemporal correlations of the data. In terms of efficiency, almost all of them reported detection rates above 85% and in several cases up to 100% but in specific conditions; with application areas especially related to environmental monitoring and care. Finally, the most relevant limitations encountered include high computational complexity and high resource consumption, sensitivity to parameters, lack of scalability, and dependence on specific assumptions about data distribution.