Title: Machine learning-based fault detection scheme for IoT-enabled WSNs

Authors: Pravindra Shekhar Shakunt; Siba K. Udgata

Addresses: School of Computer and Information Sciences, University of Hyderabad, Hyderabad 500046, India ' School of Computer and Information Sciences, University of Hyderabad, Hyderabad 500046, India

Abstract: Wireless sensor networks (WSNs) encounter faults due to their deployment in non-deterministic and potentially hazardous environments. The process of fault identification in WSNs poses several challenges. This complexity makes it challenging to pinpoint and diagnose faults within IoT-enabled WSNs. This paper uses extra tree and state-of-the-art machine learning classifiers to classify commonly occurring faults such as offset, drift, gain, data loss, stuck, random, and out-of-bounds at the sensor node level. First, we realistically induce the faults with different intensities to a benchmark dataset of temperature and humidity sensors. We propose sliding window-based data pre-processing techniques and various machine learning algorithms for classifying types of faults. The performance of the proposed scheme and other machine learning approaches are compared based on specificity, precision, recall, accuracy, F1-score, and AUC-ROC performance evaluation metric. Experimental study shows that our proposed scheme and the extra tree machine learning approach are more effective than state-of-the-art approaches.

Keywords: wireless sensor networks; WSNs; internet of things: IoT; fault detection; classification; moving window average; machine learning; extra trees.

DOI: 10.1504/IJSNET.2024.141771

International Journal of Sensor Networks, 2024 Vol.46 No.2, pp.61 - 72

Received: 29 Aug 2023
Accepted: 02 Nov 2023
Published online: 01 Oct 2024 *

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