Fault diagnosis in mobile computing using TwinSVM

Neha Malhotra; Manju Bala; Vikram Puri

doi:10.16925/2357-6014.2022.01.11

Fault diagnosis in mobile computing using TwinSVM

Research Articles

https://doi.org/10.16925/2357-6014.2022.01.11

Neha Malhotra Ver Biografía

Lovely Professional University

Manju Bala Ver Biografía

Director, Khalsa College of Engineering & Technology, India.

Vikram Puri Ver Biografía

Researcher, Duy Tan University, Vietnam

Introduction: Mobile computing systems (MCS) comes up with the challenge of low communication bandwidth and energy due to the mobile nature of the network. These features sometimes may come up with the undesirable behaviour of the system that eventually affects the efficiency of the system.

Problem: Fault tolerance in MCS will increase the efficiency of the system even in the presence of faults.

Objective: The main objective of this work is the development of the Monitoring Framework and Fault Detection and Classification.

Methodology: For the Node Monitoring and for the detection and classification of faults in the system a neighbourhood comparison-based technique has been proposed. The proposed framework uses Twin Support Vector Machine (TWSVM) algorithm has been applied to build classifier for fault classification in the mobile network.

Results: The proposed system has been compared with the existing techniques and has been evaluated towards calculating the detection accuracy, latency, energy consumption, packet delivery ratio, false classification rate and false positive rate.

Conclusion: The proposed framework performs better in terms of all the selected parameters.

Keywords: TwinSVM, faults, fault tolerance, mobile computing, fault classification, fault diagnosis

R. R. Swain and P. M. Khilar, “Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks,” Wirel. Pers. Commun., vol. 95, no. 3, pp. 2507–2548, 2017. doi: https://doi.org/10.1007/s11277-016-3931-3

E. Moridi, M. Haghparast, M. Hosseinzadeh, and S. J. Jassbi, “Fault management frameworks in wireless sensor networks: A survey,” Comput. Commun., vol. 155, pp. 205–226, 2020. doi: https://doi.org/10.1016/j.comcom.2020.03.011.

S. Chessa and P. Santi, “Comparison-based system-level fault diagnosis in ad hoc networks,” Proc. 20th IEEE Symp. Reliab. Distrib. Syst., pp. 257–266, 2001. doi: https://doi.org/10.1109/RELDIS.2001.970776

M. Elhadef, A. Boukerche, and H. Elkadiki, “A distributed fault identification protocol for wireless and mobile ad hoc networks,” J. Parallel Distrib. Comput., vol. 68, no. 3, pp. 321–335, 2008. doi: https://doi.org/10.1016/j.jpdc.2007.05.016

T. Muhammed and R. A. Shaikh, “An analysis of fault detection strategies in wireless sensor networks,” J. Netw. Comput. Appl., vol. 78, pp. 267–287, 2017. doi: https://doi.org/10.1016/j.jnca.2016.10.019

S. Zidi, T. Moulahi, and B. Alaya, “Fault detection in wireless sensor networks through SVM classifier,” IEEE Sens. J., vol. 18, no. 1, pp. 340–347, 2018. doi: https://doi.org/10.1109/JSEN.2017.2771226

M. Panda and P. M. Khilar, “Distributed Byzantine fault detection technique in wireless sensor networks based on hypothesis testing,” Comput. Electr. Eng., vol. 48, pp. 270–285, 2015. doi: https://doi.org/10.1016/j.compeleceng.2015.06.024

U. Saeed, S. U. Jan, Y. D. Lee, and I. Koo, “Fault diagnosis based on extremely randomized trees in wireless sensor networks,” Reliab. Eng. Syst. Saf., vol. 205, no. October 2020, pp. 107284, 2021. doi: https://doi.org/10.1016/j.ress.2020.107284

R. R. Swain, P. M. Khilar, and S. K. Bhoi, “Heterogeneous fault diagnosis for wireless sensor networks,” Ad Hoc Networks, vol. 69, pp. 15–37, 2018. doi: https://doi.org/10.1016/j.adhoc.2017.10.012

R. R. Swain, T. Dash, and P. M. Khilar, “A complete diagnosis of faulty sensor modules in a wireless sensor network,” Ad Hoc Networks, vol. 93, pp. 101924, 2019. doi: https://doi.org/10.1016/j.adhoc.2019.101924

S. K. Ghalem, B. Kechar, A. Bounceur, and R. Euler, “A probabilistic multivariate copula-based technique for faulty node diagnosis in wireless sensor networks,” J. Netw. Comput. Appl., vol. 127, pp. 9–25, 2019. doi: https://doi.org/10.1016/j.jnca.2018.11.009

S. U. Jan, Y. D. Lee, and I. S. Koo, “A distributed sensor-fault detection and diagnosis framework using machine learning,” Inf. Sci. (Ny)., vol. 547, pp. 777–796, 2021. doi: https://doi.org/10.1016/j.ins.2020.08.068

C. Peng, Y. Zhou, and Q. Hui, “Distributed fault diagnosis of networked dynamical systems with time-varying topology,” J. Franklin Inst., vol. 356, no. 11, pp. 5754–5780, 2019. doi: https://doi.org/10.1016/j.jfranklin.2019.05.027

S. Shao, S. Guo, and X. Qiu, “Distributed fault detection based on credibility and cooperation for wsns in smart grids,” Sensors (Switzerland), vol. 17, no. 5, pp. 983, 2017. doi: https://doi.org/10.3390/s17050983

Z. Noshad et al., “Fault detection in wireless sensor networks through the random forest classifier,” Sensors (Switzerland), vol. 19, no. 7, pp. 1–21, 2019. doi: https://doi.org /10.3390/s19071568

N. Wang, J. Wang, and X. Chen, “A trust-based formal model for fault detection in wireless sensor networks,” Sensors (Switzerland), vol. 19, no. 8, pp. 1–20, 2019. doi: https://doi.org/10.3390/s19081916

W. Hongying, D. M. Blough, and L. Alkalaj, “Analysis and experimental evaluation of comparison-based system-level diagnosis for multiprocessor systems,” Dig. Pap. - Int. Symp. Fault-Tolerant Comput., pp. 55–64, 1994. doi: https://doi.org /10.1109/FTCS.1994.315657

I. G. A. Poornima and B. Paramasivan, “Anomaly detection in wireless sensor network using machine learning algorithm,” Comput. Commun., vol. 151, no. August 2019, pp. 331–337, 2020. doi: https://doi.org/10.3390/s21144946

Jayadeva, R. Khemchandani, and S. Chandra, “Twin support vector machines for pattern classification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 5, pp. 905–910, 2007. doi: https://doi.org/10.1007/978-981-13-1132-1_42

H. Huang, X. Wei, and Y. Zhou, “Twin support vector machines: A survey,” Neurocomputing, vol. 300, pp. 34–43, 2018. doi: https://doi.org/10.1016/j.neucom.2018.01.093

K. J. Kim, “Financial time series forecasting using support vector machines,” Neurocomputing, vol. 55, no. 1–2, pp. 307–319, 2003. doi: https://doi.org/10.1016/S0925-2312(03)00372-2

[1]

N. Malhotra, M. Bala, and V. Puri, “Fault diagnosis in mobile computing using TwinSVM”, ing. Solidar, vol. 18, no. 1, pp. 1–28, Apr. 2022, doi: 10.16925/2357-6014.2022.01.11.

Download Citation

This work is licensed under a Creative Commons Attribution 4.0 International License.

Cession of rights and ethical commitment

As the author of the article, I declare that is an original unpublished work exclusively created by me, that it has not been submitted for simultaneous evaluation by another publication and that there is no impediment of any kind for concession of the rights provided for in this contract.

In this sense, I am committed to await the result of the evaluation by the journal Ingeniería Solidaría before considering its submission to another medium; in case the response by that publication is positive, additionally, I am committed to respond for any action involving claims, plagiarism or any other kind of claim that could be made by third parties.

At the same time, as the author or co-author, I declare that I am completely in agreement with the conditions presented in this work and that I cede all patrimonial rights, in other words, regarding reproduction, public communication, distribution, dissemination, transformation, making it available and all forms of exploitation of the work using any medium or procedure, during the term of the legal protection of the work and in every country in the world, to the Universidad Cooperativa de Colombia Press.

Issue

Vol. 18 No. 1 (2022)

Published

2022-04-30

Downloads

PDF

Metrics

Metrics Loading ...

https://plu.mx/plum/a/?doi=10.16925/2357-6014.2022.01.11