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An Improved Sparrow Search Algorithm for Node Localization in WSN

R. Thenmozhi1, Abdul Wahid Nasir2, Vijaya Krishna Sonthi3, T. Avudaiappan4, Seifedine Kadry5, Kuntha Pin6, Yunyoung Nam7,*

1 Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, India
2 Department of Electronics Communication and Engineering, CMR Institute of Technology, Bengaluru, 560037, India
3 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522502, Andhra Pradesh, India
4 Department of Computer Science and Engineering, K. Ramakrishnan College of Technology, Tiruchirappalli, 621112, India
5 Department of Applied Data Science, Noroff University College, Kristiansand, Norway
6 Department of ICT Convergence, Soonchunhyang University, Korea
7 Department of Computer Science and Engineering, Soonchunhyang University, Korea

* Corresponding Author: Yunyoung Nam. Email: email

Computers, Materials & Continua 2022, 71(1), 2037-2051.


Wireless sensor networks (WSN) comprise a set of numerous cheap sensors placed in the target region. A primary function of the WSN is to avail the location details of the event occurrences or the node. A major challenge in WSN is node localization which plays an important role in data gathering applications. Since GPS is expensive and inaccurate in indoor regions, effective node localization techniques are needed. The major intention of localization is for determining the place of node in short period with minimum computation. To achieve this, bio-inspired algorithms are used and node localization is assumed as an optimization problem in a multidimensional space. This paper introduces a new Sparrow Search Algorithm with Doppler Effect (SSA-DE) for Node Localization in Wireless Networks. The SSA is generally stimulated by the group wisdom, foraging, and anti-predation behaviors of sparrows. Besides, the Doppler Effect is incorporated into the SSA to further improve the node localization performance. In addition, the SSA-DE model defines the position of node in an iterative manner using Euclidian distance as the fitness function. The presented SSA-DE model is implanted in MATLAB R2014. An extensive set of experimentation is carried out and the results are examined under a varying number of anchor nodes and ranging error. The attained experimental outcome ensured the superior efficiency of the SSA-DE technique over the existing techniques.


Cite This Article

R. Thenmozhi, A. Wahid Nasir, V. Krishna Sonthi, T. Avudaiappan, S. Kadry et al., "An improved sparrow search algorithm for node localization in wsn," Computers, Materials & Continua, vol. 71, no.1, pp. 2037–2051, 2022.

cc 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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