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Enhanced gray wolf optimization for estimation of time difference of arrival in WSNs

Devika E. (Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, India)
Saravanan A. (Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, India)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 30 August 2022

51

Abstract

Purpose

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

Design/methodology/approach

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

Findings

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

Originality/value

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

Keywords

Acknowledgements

Statements and declarations:

Ethical approval: This article does not contain any studies with animals performed by any of the authors.

Conflict of interest: The authors declare that they have no conflict of interest.

Informed consent: Informed consent was obtained from all individual participants included in the study.

Availability of data and material: All data available in manuscript.

Code availability: All data available in manuscript – Custom Mode.

Citation

E., D. and A., S. (2022), "Enhanced gray wolf optimization for estimation of time difference of arrival in WSNs", International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPCC-05-2022-0181

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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