Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Abierto Inglés 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. metadata Uppal, Mudita and Gupta, Deepali and Anand, Divya and S. Alharithi, Fahd and Almotiri, Jasem and Ortega-Mansilla, Arturo and Singh, Dinesh and Goyal, Nitin mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, arturo.ortega@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2022) Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve. Computers, Materials & Continua, 72 (1). pp. 1799-1814. ISSN 1546-2226

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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.

Item Type: Article
Uncontrolled Keywords: Fault identification; fault classification; IoT sensor nodes; analog sensors; digital sensors; fall curve
Subjects: Subjects > Engineering
Divisions: Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Date Deposited: 06 May 2022 23:55
Last Modified: 12 Jul 2023 23:30
URI: https://repositorio.unini.edu.mx/id/eprint/652

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