Outlier detection strategies for WSNs: A survey
Wireless Sensor Networks (WSNs) are developed significantly from the last decades and attracted the attention of scientific and industrial domains. In WSNs, sensor nodes distributed autonomously in harsh environments are easily vulnerable to faults and attacks that cause sensor readings unreliable a...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821000513 |
| _version_ | 1817999752743092224 |
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| author | Bhanu Chander G. Kumaravelan |
| author_facet | Bhanu Chander G. Kumaravelan |
| author_sort | Bhanu Chander |
| collection | DOAJ |
| description | Wireless Sensor Networks (WSNs) are developed significantly from the last decades and attracted the attention of scientific and industrial domains. In WSNs, sensor nodes distributed autonomously in harsh environments are easily vulnerable to faults and attacks that cause sensor readings unreliable and inaccurate. In this scenario, sensor readings that have differed considerably from healthy behaviors will be considered abnormal data or anomalies/outliers. The inclusion of such outliers in data analytics will inevitably affect the outcome of the decision-making process. Thus, detecting outliers in WSNs using data-driven approaches becomes a novel technique among the Machine Learning (ML) communities. Meanwhile, various research issues are there in measuring the performance of the deployed ML algorithms in detecting outliers in WSNs, which generally contains minimum resources in terms of computational capability and power sources to ensure data quality. Hence, this paper presents a comprehensive overview of the state-of-the-art Statistical and Artificial Intelligence (AI) based techniques used in WSNs to detect outliers in the view of architecture, type, degree, approach, and detection mode. Furthermore, each aforementioned outlier detection approach is presented with detailed discussions and future scope for developments. |
| first_indexed | 2024-04-14T03:13:14Z |
| format | Article |
| id | doaj.art-fcc7cb2145a645e9b84f6b6eede8626b |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-14T03:13:14Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-fcc7cb2145a645e9b84f6b6eede8626b2022-12-22T02:15:33ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134856845707Outlier detection strategies for WSNs: A surveyBhanu Chander0G. Kumaravelan1Research Scholar, Department of Computer Science, School of Engineering & Technology, Pondicherry University, Karaikal Campus, India; Corresponding author.Department of Computer Science, School of Engineering & Technology, Pondicherry University, Karaikal Campus, IndiaWireless Sensor Networks (WSNs) are developed significantly from the last decades and attracted the attention of scientific and industrial domains. In WSNs, sensor nodes distributed autonomously in harsh environments are easily vulnerable to faults and attacks that cause sensor readings unreliable and inaccurate. In this scenario, sensor readings that have differed considerably from healthy behaviors will be considered abnormal data or anomalies/outliers. The inclusion of such outliers in data analytics will inevitably affect the outcome of the decision-making process. Thus, detecting outliers in WSNs using data-driven approaches becomes a novel technique among the Machine Learning (ML) communities. Meanwhile, various research issues are there in measuring the performance of the deployed ML algorithms in detecting outliers in WSNs, which generally contains minimum resources in terms of computational capability and power sources to ensure data quality. Hence, this paper presents a comprehensive overview of the state-of-the-art Statistical and Artificial Intelligence (AI) based techniques used in WSNs to detect outliers in the view of architecture, type, degree, approach, and detection mode. Furthermore, each aforementioned outlier detection approach is presented with detailed discussions and future scope for developments.http://www.sciencedirect.com/science/article/pii/S1319157821000513Wireless sensor networkOutlier detectionClassificationData-driven approaches |
| spellingShingle | Bhanu Chander G. Kumaravelan Outlier detection strategies for WSNs: A survey Journal of King Saud University: Computer and Information Sciences Wireless sensor network Outlier detection Classification Data-driven approaches |
| title | Outlier detection strategies for WSNs: A survey |
| title_full | Outlier detection strategies for WSNs: A survey |
| title_fullStr | Outlier detection strategies for WSNs: A survey |
| title_full_unstemmed | Outlier detection strategies for WSNs: A survey |
| title_short | Outlier detection strategies for WSNs: A survey |
| title_sort | outlier detection strategies for wsns a survey |
| topic | Wireless sensor network Outlier detection Classification Data-driven approaches |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821000513 |
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