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A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

School of Computing Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Department of Software Engineering & Information System, Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia
College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia
RIADI Laboratory, University of Manouba, Manouba 2010, Tunisia
Department of Computing, University of Glouctershire, Cheltenham GL50 2RH, UK
Fakultät Electronic und Informatik, Gottfried Wilhelm Leibniz Universität Hannover, 30167 Hannover, Germany
Author to whom correspondence should be addressed.
Symmetry 2020, 12(3), 328;
Received: 26 December 2019 / Revised: 2 February 2020 / Accepted: 3 February 2020 / Published: 25 February 2020
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. View Full-Text
Keywords: systematic literature review; outlier detection; wireless sensor networks systematic literature review; outlier detection; wireless sensor networks
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MDPI and ACS Style

Safaei, M.; Asadi, S.; Driss, M.; Boulila, W.; Alsaeedi, A.; Chizari, H.; Abdullah, R.; Safaei, M. A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks. Symmetry 2020, 12, 328.

AMA Style

Safaei M, Asadi S, Driss M, Boulila W, Alsaeedi A, Chizari H, Abdullah R, Safaei M. A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks. Symmetry. 2020; 12(3):328.

Chicago/Turabian Style

Safaei, Mahmood, Shahla Asadi, Maha Driss, Wadii Boulila, Abdullah Alsaeedi, Hassan Chizari, Rusli Abdullah, and Mitra Safaei. 2020. "A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks" Symmetry 12, no. 3: 328.

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