Open Access

ARTICLE

RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

by Weiwei Hu¹, Kiran Sree Pokkuluri², Rajesh Arunachalam^3,*, Bander A. Jabr⁴, Yasser A. Ali⁴, Preethi Palanisamy⁵

1 School of Information Engineering, Technology & Media, University of Henan, Kaifeng, 475004, China
2 Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, 534202, Andhra Pradhesh, India
3 Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, 602105, Chennai, India
4 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
5 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, 602105, Chennai, India

* Corresponding Author: Rajesh Arunachalam. Email:

Computers, Materials & Continua 2024, 81(3), 4833-4851. https://doi.org/10.32604/cmc.2024.055561

Received 01 July 2024; Accepted 19 September 2024; Issue published 19 December 2024

Abstract

Wireless Sensor Network (WSNs) consists of a group of nodes that analyze the information from surrounding regions. The sensor nodes are responsible for accumulating and exchanging information. Generally, node localization is the process of identifying the target node’s location. In this research work, a Received Signal Strength Indicator (RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models. Initially, the RSSI value is identified using the Deep Neural Network (DNN). The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process, also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN. The position of the anchor nodes is fixed for detecting the location of the target. Further, the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm (HTCI-LEO). During the node localization process, the average localization error is minimized, which is the objective of the optimal node localization. In the regular and irregular surfaces, this hybrid algorithm effectively performs the localization process. The suggested hybrid algorithm converges very fast in the three-dimensional (3D) environment. The accuracy of the proposed node localization process is 94.25%.

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Keywords

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