A probabilistic multivariate copula-based technique for faulty node diagnosis in wireless sensor networks

Ghalem, Sanaa Kawther; Kechar, Bouabdellah; Bounceur, Ahcène; Euler, Reinhardt

doi:10.1016/j.jnca.2018.11.009

Cited by 10 publications

(11 citation statements)

References 33 publications

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“…This choice is justified by the fact that this algorithm has shown its usefulness in this context of use, which has been proven in the recently conducted research. 35 After the neighbor selection phase, adaptive entropy and a greedy algorithm are applied to the data that have the same timestamp as their neighbors, and this in order to select the sensor that can collaborate more effectively with the considered sensor to globally detect noise in the considered network.…”

Section: Reading Sensory Datamentioning

confidence: 99%

Global outliers detection in wireless sensor networks: A novel approach integrating time‐series analysis, entropy, and random forest‐based classification.

et al. 2021

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Wireless sensor networks (WSNs) have recently attracted greater attention worldwide due to their practicality in monitoring, communicating, and reporting specific physical phenomena. The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signal weakness, or intrusion attacks depending on the specific situation. Sending high-noise data has negative effects not just on data accuracy and network reliability, but also regarding the decision-making processes in the base station.Anomaly detection, or outlier detection, is the process of detecting noisy data amidst the contexts thus described. The literature contains relatively few noise detection techniques in the context of WSNs, particularly for outlier-detection algorithms applying time series analysis, which considers the effective neighbors to ensure a global-collaborative detection. Hence, the research presented in this article is intended to design and implement a global outlier-detection approach, which allows us to find and select appropriate neighbors to ensure an adaptive collaborative detection based on time-series analysis and entropy techniques. The proposed approach applies a random forest algorithm for identifying the best results. To measure the effectiveness and efficiency of the proposed approach, a comprehensive and real scenario provided by the Intel Berkeley Research Laboratory has been simulated. Noisy data have been injected into the collected data randomly. The results obtained from the experiment then conducted experimentation demonstrate that our approach can detect anomalies with up to 99% accuracy.

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Section: Reading Sensory Datamentioning

confidence: 99%

Global outliers detection in wireless sensor networks: A novel approach integrating time‐series analysis, entropy, and random forest‐based classification.

et al. 2021

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“…This calculation of density can be executed in an evenly distributed manner. In [35], a Local Outlier Factor (LOF) method is proposed. This method consists of drawing a circle around "k" measures, where depending on the density level obtained, it attributes an "outlier metric" parameter to each measure, which determines whether or not each such measure should be defined as an outlier.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, for the neighbor selection, a simulation of the Monte Carlo algorithm is conducted. This choice is justified by the fact that this algorithm has shown its usefulness in this context of use, which has been proven in the recently conducted research [35]. After the neighbor selection phase, adaptive entropy and a greedy algorithm are applied to the data that have the same timestamp as their neighbors, and this in order to select the sensor that can collaborate more effectively with the considered sensor to globally detect noise in the considered network.…”

Section: Reading Sensory Datamentioning

confidence: 99%

Global Outliers Detection in Wireless Sensor Networks: A Novel Approach Integrating Time-Series Analysis, Entropy, and Random Forest-based Classification

Safaei¹,

Driss²,

Boulila³

et al. 2021

Preprint

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Wireless Sensor Networks (WSNs) have recently attracted greater attention worldwide due to their practicality in monitoring, communicating, and reporting specific physical phenomena. The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signal weakness, or intrusion attacks depending on the specific situation. Sending high-noise data has negative effects not just on data accuracy and network reliability, but also regarding the decision-making processes in the base station.Anomaly detection, or outlier detection, is the process of detecting noisy data amidst the contexts thus described. The literature contains relatively few noise detection techniques in the context of WSNs, particularly for outlier-detection algorithms applying time series analysis, which considers the effective neighbors to ensure a global-collaborative detection. Hence, the research presented in this paper is intended to design and implement a global outlier-detection approach, which allows us to find and select appropriate neighbors to ensure an adaptive collaborative detection based on time-series analysis and entropy techniques.The proposed approach applies a random forest algorithm for identifying the best results. To measure the effectiveness and efficiency of the proposed approach, a comprehensive and real scenario provided by the Intel Berkeley Re-

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“…In Reference , a technique, labeled as local outlier factor (LOF), is proposed. It first marks around at minimum k ‐measures called “outlier metric,” outlier metric dependent on the obtained level of density it adds to sensory data to identify if this measure is an outlier or possibly not.…”

Section: Outlier Detection In Wsnsmentioning

confidence: 99%

“…The value 10 is chosen as a starting point to calculate features after reading data from the sensor node. Computed features are represented by the matrix feaMatrix illustrated by Equation (31). This matrix will be used by the prediction algorithm function.…”

mentioning

confidence: 99%

Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

Safaei

Ismail

Chizari³

et al. 2020

Softw Pract Exp

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Summary Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time‐series‐based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well‐known algorithms using benchmark real‐life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.

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A probabilistic multivariate copula-based technique for faulty node diagnosis in wireless sensor networks

Cited by 10 publications

References 33 publications

Global outliers detection in wireless sensor networks: A novel approach integrating time‐series analysis, entropy, and random forest‐based classification.

Global outliers detection in wireless sensor networks: A novel approach integrating time‐series analysis, entropy, and random forest‐based classification.

Global Outliers Detection in Wireless Sensor Networks: A Novel Approach Integrating Time-Series Analysis, Entropy, and Random Forest-based Classification

Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

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