Open Access

ARTICLE

Design of Clustering Techniques in Cognitive Radio Sensor Networks

by R. Ganesh Babu^1,*, D. Hemanand², V. Amudha³, S. Sugumaran⁴

1 Department of Electronics and Communication Engineering, SRM TRP Engineering College, 621105, Tamil Nadu, India
2 Department of Computer Science and Engineering, Sriram Engineering College, 602024, Tamil Nadu, India
3 Department of Electronics and Communication Engineering, Saveetha School of Engineering, 602105, Tamil Nadu, India
4 Department of Electronics and Communication Engineering, Vishnu Institute of Technology, Bhimavaram, 534202, Andhra Pradesh, India

* Corresponding Author: R. Ganesh Babu. Email:

Computer Systems Science and Engineering 2023, 44(1), 441-456. https://doi.org/10.32604/csse.2023.024049

Received 01 October 2021; Accepted 05 January 2022; Issue published 01 June 2022

Abstract

In recent decades, several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during transmission to a shorter distance while restricting the Primary Users (PUs) interference. The Cognitive Radio (CR) system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm (ASDIC) that shows better spectrum sensing among group of multiusers in terms of sensing error, power saving, and convergence time. In this research paper, the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity. In this research, multiple random Secondary Users (SUs), and PUs are considered for implementation. Hence, the proposed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algorithms. Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646% compared to the existing algorithms. Similarly, ASDIC algorithm reduced 24.23% of SUs average node power compared to the existing algorithms. Probability of detection is higher by reducing the Signal-to-Noise Ratio (SNR) to 2 dB values. The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection. Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of network capacity.

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Keywords

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