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A systematic literature review on outlier detection in wireless sensor networks

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 collect...

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Main Authors: Safaei, Mahmood, Asadi, Shahla, Driss, Maha, Boulila, Wadii, Alsaeedi, Abdullah, Chizari, Hassan, Abdullah, Rusli, Safaei, Mitra
Format: Article
Language:English
Published: MDPI 2020
Online Access:http://psasir.upm.edu.my/id/eprint/38136/1/38136.pdf
http://psasir.upm.edu.my/id/eprint/38136/
https://www.mdpi.com/2073-8994/12/3/328
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spelling my.upm.eprints.381362020-05-03T22:55:58Z http://psasir.upm.edu.my/id/eprint/38136/ A systematic literature review on outlier detection in wireless sensor networks Safaei, Mahmood Asadi, Shahla Driss, Maha Boulila, Wadii Alsaeedi, Abdullah Chizari, Hassan Abdullah, Rusli Safaei, Mitra 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. MDPI 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/38136/1/38136.pdf Safaei, Mahmood and Asadi, Shahla and Driss, Maha and Boulila, Wadii and Alsaeedi, Abdullah and Chizari, Hassan and Abdullah, Rusli and Safaei, Mitra (2020) A systematic literature review on outlier detection in wireless sensor networks. Symmetry, 12 (3). art. no. 328. pp. 1-40. ISSN 2073-8994 https://www.mdpi.com/2073-8994/12/3/328 10.3390/sym12030328
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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.
format Article
author Safaei, Mahmood
Asadi, Shahla
Driss, Maha
Boulila, Wadii
Alsaeedi, Abdullah
Chizari, Hassan
Abdullah, Rusli
Safaei, Mitra
spellingShingle Safaei, Mahmood
Asadi, Shahla
Driss, Maha
Boulila, Wadii
Alsaeedi, Abdullah
Chizari, Hassan
Abdullah, Rusli
Safaei, Mitra
A systematic literature review on outlier detection in wireless sensor networks
author_facet Safaei, Mahmood
Asadi, Shahla
Driss, Maha
Boulila, Wadii
Alsaeedi, Abdullah
Chizari, Hassan
Abdullah, Rusli
Safaei, Mitra
author_sort Safaei, Mahmood
title A systematic literature review on outlier detection in wireless sensor networks
title_short A systematic literature review on outlier detection in wireless sensor networks
title_full A systematic literature review on outlier detection in wireless sensor networks
title_fullStr A systematic literature review on outlier detection in wireless sensor networks
title_full_unstemmed A systematic literature review on outlier detection in wireless sensor networks
title_sort systematic literature review on outlier detection in wireless sensor networks
publisher MDPI
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/38136/1/38136.pdf
http://psasir.upm.edu.my/id/eprint/38136/
https://www.mdpi.com/2073-8994/12/3/328
_version_ 1665895957061959680
score 13.211869

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