Detection and accommodation of outliers in Wireless Sensor Networks within a multi-agent framework

Gil, Paulo; Martins, Hugo; Januário, Filipa

doi:10.1016/j.asoc.2015.12.042

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Data that deviate significantly from the other data of any group is called outlier or anomaly (Bosman et al, 2017). Malicious activities or abnormal behavior of the sensors can cause such anomalies (Ghorbel et al, 2015;Gil et al, 2016;Dwivedi et al, 2018). Healthcare data contain the patients' health records and any alteration to these data may create serious problems.…”

Section: Related Workmentioning

confidence: 99%

A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

Dwivedi

Kumar

Buyya

2021

International Journal of Healthcare Information Systems and Informatics

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A smart healthcare sensor cloud is an amalgamation of the body sensor networks and the cloud that facilitates the early diagnosis of diseases and the real-time monitoring of patients. Sensitive data of the patients which are stored in the cloud must be free from outliers that may be caused by malfunctioned hardware or the intruders. This paper presents a machine learning-based scheme for outlier detection in smart healthcare sensor clouds. The proposed scheme is a hybrid of clustering and classification techniques in which a two-level framework is devised to identify the outliers precisely. At the first level, a density-based scheme is used for clustering while at the second level, a Gaussian distribution-based approach is used for classification. This scheme is implemented in Python and compared with a clustering-based approach (Mean Shift) and a classification-based approach (Support Vector Machine) on two different standard datasets. The proposed scheme is evaluated on various performance metrics. Results demonstrate the superiority of the proposed scheme over the existing ones.

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Section: Related Workmentioning

confidence: 99%

A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

Dwivedi

Kumar

Buyya

2021

International Journal of Healthcare Information Systems and Informatics

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“…Four techniques were used including Mahalanobis Distance (MD), local outlier factor, OCSVM and hierarchical clustering, in which OCSVM gives the best results and outperforms on all other techniques. Gil et al [21] have introduced three techniques for online anomaly detection in WSNs including, PCA, machine learning, and univariate statistic approach. These techniques are executed within a hierarchical multi-agent system.…”

Section: Related Workmentioning

confidence: 99%

High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study

Shia¹,

Tawfeeq²,

Mahmoud³

2019

IJMECS

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The rapid development of Smart Cities and the Internet of Thinks (IoT) is largely dependent on data obtained through Wireless Sensor Networks (WSNs). The quality of data gathered from sensor nodes is influenced by abnormalities that happen due to different reasons including, malicious attacks, sensor malfunction or noise related to communication channel. Accordingly, outlier detection is an essential procedure to ensure the quality of data derived from WSNs. In the modern utilizations of WSNs, especially in online applications, the high detection rate for abnormal data is closely correlated with the time required to detect these data. This work presents an investigation of different outlier detection techniques and compares their performance in terms of accuracy, true positive rate, false positive rate, and the required detection time. The investigated algorithms include Particle Swarm Optimization (PSO), Deferential Evolution (DE), One Class Support Vector Machine (OCSVM), K-means clustering, combination of Contourlet Transform and OCSVM (CT-OCSVM), and combination of Discrete Wavelet Transform and OCSVM (DWT-OCSVM). Real datasets gathered from a WSN configured in a local lab are used for testing the techniques. Different types and values of outliers have been imposed in these datasets to accommodate the comparison requirements. The results show that there are some differences in the accuracy, detection rate, and false positive rate of the outlier detections, except K-means clustering which failed to detect outlier in some cases. The required detection time for both PSO and DE is very long as compared with the other techniques meanwhile, the CT-OCSVM and DWT-OCSVM required short time and also they can achieve high performance. On the other hand CT and DWT technique has the ability to compress its used dataset where in this paper, CT can extract much less number of coefficients as compared DWT. This makes CT-OCSVM more efficient to be utilized in detecting outliers in WSNS.

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“…In addition, the method proposed for outliers detection in wireless sensor networks has been studied, which is implemented within a hierarchical multi-agent framework in ref. [27] . It is worth mentioning that the 𝐻 ∞ consensus control problem based on such a novel observer for linear multi-agent systems has not yet gained adequate attention, and this constitutes the main motivation of this paper.…”

Section: Introductionmentioning

confidence: 99%

Observer-based H∞ consensus for linear multi-agent systems subject to measurement outliers

Lu¹,

Karimi²,

Zhang³

2022

J Smart Environ Green Comput

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Detection and accommodation of outliers in Wireless Sensor Networks within a multi-agent framework

Cited by 14 publications

References 19 publications

A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study

Observer-based H∞ consensus for linear multi-agent systems subject to measurement outliers

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