Prashanth C. R.

Work place: Department of Electronics & Telecommunication Engineering, Dr. Ambedkar Institute of Technology, Bengaluru-560056, Karnataka, India



Research Interests: Biometrics, Computational Engineering, Pattern Recognition, Computer Networks


Prashanth C. R. received his BE degree in electronics, ME degree in digital communication, and the PhD degree from Bangalore University, Bangalore, India. He is currently working at the Department of Electronics & Telecommunication Engineering and is Professor and Dean (Examinations) at Dr. Ambedkar Institute of Technology, Bengaluru-560056, Karnataka, India. His areas of interest are image processing, pattern recognition, biometrics, computer networks, communication engineering, device modelling. and engineering education. He has over 50 research publications in refereed International Journals and Conference Proceedings.  

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:, 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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