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

Safaei, Mahmood, Ismail, Abul Samad, Chizari, Hassan ORCID: 0000-0002-6253-1822, Driss, Maha, Boulila, Wadii, Asadi, Shahla and Safaei, Mitra (2020) Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach. Journal of Software: Practice and Experience. ISSN 0038-0644 (In Press)

Text (peer-review article) 8094 Chizari (2020) Standalone noise and anomaly detection in wireless sensor networks.pdf - Accepted Version Restricted to Repository staff only until 10 January 2021. (Publisher Embargo). Available under License All Rights Reserved. Download (1MB)

Abstract

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.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Anomaly detection; Outlier detection; Time-series analysis; Wireless sensor network
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Schools and Research Institutes > School of Business and Technology > Technical & Applied Computing
Research Priority Areas:	Applied Business & Technology
Depositing User:	Susan Turner
Date Deposited:	30 Jan 2020 11:27
Last Modified:	30 Jan 2020 11:30
URI:	http://eprints.glos.ac.uk/id/eprint/8094

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