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Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

Mudita Uppal1, Deepali Gupta1, Divya Anand2, Fahd S. Alharithi3, Jasem Almotiri3, Arturo Mansilla4,5, Dinesh Singh6, Nitin Goyal1,*
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
2 Computer Science & Engineering Department, Lovely Professional University, Jalandhar, Punjab, India
3 Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
4 Higher Polytechnic School/Industrial Organization Engineering, Universidad Europea del Atlántico, Isabel Torres 21, 39011, Santander, Spain
5 Department of Project Management, Universidad Internacional Iberoamericana, Campeche, 24560, Mexico
6 Computer Science and Engineering Department, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat, Haryana, India
* Corresponding Author: Nitin Goyal. Email:

Computers, Materials & Continua 2022, 72(1), 1799-1814. https://doi.org/10.32604/cmc.2022.025330

Received 20 November 2021; Accepted 11 January 2022; Issue published 24 February 2022

Abstract

The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form. Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described. There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique. So, some solutions are provided to overcome the limitations of the fall curve technique. In this paper, a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years. Its novelty is to predict a fault before its occurrence by looking at the fall curve. The sensing of current flow in devices is important to prevent a major loss. So, the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices. The analysis result proved that if any of the current sensors gets faulty, then the fall curve will differ and the value will immediately drop to zero. Various evaluation metrics for fault prediction are also described in this paper. At last, this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.

Keywords

Fault identification; fault classification; IoT sensor nodes; analog sensors; digital sensors; fall curve

Cite This Article

M. Uppal, D. Gupta, D. Anand, F. S. Alharithi, J. Almotiri et al., "Fault pattern diagnosis and classification in sensor nodes using fall curve," Computers, Materials & Continua, vol. 72, no.1, pp. 1799–1814, 2022.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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