Darshan B. D.

Work place: Department of Electronics and Communication Engineering, S J B Institute of Technology, Kengeri, Bengaluru-560060, Karnataka, India

E-mail: darshan156@gmail.com


Research Interests: Sensor, Wireless Networks


Darshan B. D. received his BE degree in Electronics & Communication Engineering from Visvesvaraya Technological University, Belgaum in 2008 and his MTech degree in Digital Electronics & Communication System from Visvesvaraya Technological University, Belgaum, India in 2011. He is currently working as the Assistant Professor at the Department of Electronics and Communication Engineering, S J B Institute of Technology, Kengeri, Bengaluru-560060, Karnataka, India. His research interests include computer networking, communication engineering, and wireless sensor networks.

Author Articles
Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-based Routing in WSN Assisted IoT

By Darshan B. D. Prashanth C. R.

DOI: https://doi.org/10.5815/ijcnis.2023.05.09, Pub. Date: 8 Oct. 2023

Wireless sensor network (WSN) efficiently sends and receives the data on the internet of things (IoT) environment. As a large-scale WSN's nodes are powered by batteries, it is essential to create an energy-efficient system to decrease energy consumption and increase the network's lifespan. The existing methods not present effectual cluster head (CH) selection and trust node computation. Therefore, dual-discriminator conditional generative adversarial network optimized with a hybrid Momentum search algorithm and Giza Pyramids Construction algorithm for Cluster Based Routing in WSN Assisted IoT is proposed in this manuscript, for securing data transmission by identifying the optimum CH in the network (DDcGAN-MSA-GPCA-CBR-WSN-IoT). Initially, the proposed method is acting routing process via cluster head. Therefore, Dual-Discriminator conditional Generative Adversarial Network (DDcGAN) is considered to select the CH depending on multi-objective fitness function. The multi-objective fitness function, such as energy, delay, throughput, distance among the nodes, cluster density, capacity, collision, traffic rate, and cluster density. Based on fitness function, CH is selected. After cluster head selection, a malicious node depends on three parameters: trust, delay, and distance. These three parameters are optimized by hyb MSA-GPCA for ideal trust path selection. The proposed DDcGAN-MSA-GPCA-WSN-IoT technique is activated in PYTHON and network simulator (NS2) tool. Its effectiveness is analyzed under performance metrics, such as number of alive nodes, dead nodes, delay, energy consumption, packet delivery ratio, a lifetime of sensor nodes, and total residual energy. The simulation outcomes display that the proposed method attains lower delay, higher packet delivery ratio and high network lifetime when comparing to the existing models.

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