Comparative Analysis of Distributive Optimized Clustering Techniques in Cognitive Radio Networks

Babu, R. Ganesh; Amudha, V.

doi:10.14419/ijet.v7i3.27.18470

Cited by 8 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study presented in this paper analyzes Distributed Swarm Optimized Clustering, distributed firefly optimized clustering, and distributed jumper firefly optimized clustering. The first method of Distributed Swarm Optimized Clustering (DSOC) proposed every group of clustering nodes moves randomly towards its best swarm particle having the least neighborhood distance [9]. Each particle's best position and velocity are evaluated according to the objective function until an optimum global best position is reached.…”

Section: Introductionmentioning

confidence: 99%

“…The second proposed method of Distributed Firefly Optimized Clustering (DFOC) has been studied. The DFOC is best known for the grouping of nodes [10]. All the cognitive nodes move towards the brighter firefly with random velocity to form an organized cluster with the least computation time.…”

Section: Introductionmentioning

confidence: 99%

“…The CR network gets spectral efficiency when cooperative features incorporated with spectrum sensing and spectrum sharing with each other. The primary network can support a cooperative leased communication network with a third party to access the licensed radio spectrum without any interferences [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Comparison of PU Number Vs Average Node Power F I G U R E 7 Comparison of SU Number Vs Average Node Power R e t r a c t e d BABU R ET AL.-9 …”

mentioning

confidence: 99%

See 3 more Smart Citations

Retracted : Comparative analysis of distributive linear and nonlinear optimized spectrum sensing clustering techniques in cognitive radio network systems

R¹,

Obaidat

Amudha

et al. 2021

IET Networks

View full text Add to dashboard Cite

In this paper, a study has been conducted to compare the performance of different heuristic optimization algorithms such as Distributed Swarm Optimized Clustering (DSOC), Distributed Firefly Optimized Clustering (DFOC) and Distributed Jumper Firefly Optimized Clustering (DJFOC) techniques used for the dynamic clustering. In DSOC, every group of clustering nodes moves towards its best swarm particle having the best neighbor location with random velocity to form an organized cluster. DFOC and DJFOC are nonlinear optimization tools based on the random attractiveness of firefly intensity behaviour with the least computation time. DJFOC is used to collect the whole situation in the current records and support to change the new appropriate situation by the status table. The DJFOC aims to save transmit power with shortest distances and less control overhead when Secondary Users (SUs) or Primary Users (PUs) changes its position. The convergence rate of DJFOC is better than the DSOC and DFOC. The results show that the proposed DJFOC has a better efficiency of 10.137% when compared to the DSOC and 2.801% with DFOC in SUs average node power. For small Signal-to-Noise Ratio (SNR) < 2 dB, probability of detection is high. In primary detection, the proposed DJFOC is yielding a low false alarm rate compared to DSOC and DFOC.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%