Open Access

ARTICLE

Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

R. Punithavathi¹, R. Thanga Selvi², R. Latha³, G. Kadiravan^4,*, V. Srikanth⁵, Neeraj Kumar Shukla⁶

1 Department of Information Technology, M.Kumarasamy College of Engineering, Karur, 639 113, India
2 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India
3 Department of ECE, HKBK College of Engineering, Nagawara, Bengaluru, 560045, India
4 School of Computer Science and Applications, REVA University, Bengaluru, 560064, India
5 Department of Computer Applications, Chandigarh Business School of Administration, CGC Laundran, 140307, Mohali, India
6 Electrical Engineering Department, King Khalid University, Abha, 62529, Saudi Arabia

* Corresponding Author: G. Kadiravan. Email:

Intelligent Automation & Soft Computing 2022, 33(1), 143-156. https://doi.org/10.32604/iasc.2022.023344

Received 04 September 2021; Accepted 18 October 2021; Issue published 05 January 2022

Abstract

Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) based intrusion detection. The OSNN technique involves the hyperparameter tuning of the traditional SNN using the MRFO algorithm and consequently increases the detection rate. In order to assess the enhanced performance of the MOMRFO-NLID technique, a series of simulations take place and the results reported superior performance compared to existing techniques interms of distinct evaluation parameters.

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Keywords

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