A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

Abstract

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data

“…The problems pointed out previously are common when time-series are analyzed, and different approaches have been published for solving them [19,22,23]. The common characteristic of most of them is that their algorithms present one or more steps in which a classification model (e.g., a regression model) must be calculated before addressing the same problem found in the other, more general, outlier identification methods.…”

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

“…Their study presents different techniques related to intrusion detections. Some studies proposed several anomaly detection techniques based on supervised, unsupervised, and clustering methods [45][46][47][48][49]. The lack of discussion and research problems in the available datasets are one of the research gaps that need to be addressed.…”

Data Quality Affecting Big Data Analytics in Smart Factories: Research Themes, Issues and Methods

“…While we have consistently followed the guidelines of Wolfswinkel et al [10] to search and select relevant articles to ensure the completeness of the data sample in this study, there may still be some publications that have not been included in our final selection. There are three limitations to consider.…”

“…RQ3: What are the methods used to address these DQ issues in the SF context? In this study, the systematic literature review (SLR) approach was selected as it offers a method for the selection, analysis and interpretation of available literature in relation to addressing a specific research topic, a RQ or a phenomenon of interest [10] that best fits this study to study and understand the state of the art on the topic of interest. We followed the guidelines proposed by Wolfswinkel et al [11] to conduct an SLR of the empirical studies that have a focus on DQ affecting BDA in the SF context (up to 2020, inclusive), with well-defined steps to provide comprehensive information to the research community.…”