Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach. (10th January 2020)

• Record Type: Journal Article
• Authors: Safaei, Mahmood; Ismail, Abul Samad; Chizari, Hassan; Driss, Maha; Boulila, Wadii; Asadi, Shahla; Safaei, Mitra
• Is Part Of: Software, practice & experience. Volume 50:Number 4(2020)
• Page Start: 428
• Page End: 446
• Publication Date: 2020-01-10
• Subjects: anomaly detection -- outlier detection -- time‐series analysis -- wireless sensor network; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2785
• Languages: English
• ISSNs: 0038-0644

Abstract

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.