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

In this study, a study has been conducted to compare the performance of different heuristic optimisation algorithms, such as distributed swarm optimised clustering (DSOC), distributed firefly optimised clustering (DFOC) and distributed jumper firefly optimised clustering (DJFOC) techniques used for the dynamic clustering. In DSOC, every group of clustering nodes moves towards its best swarm particle having the best neighbour location with random velocity to form an organised cluster. DFOC and DJFOC are non-linear optimisation tools based on the random attractiveness of firefly intensity behaviour with the least computation time. DJFOC is 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 change 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 a small signal-to-noise ratio <2 dB, the probability of detection is high. In primary detection, the proposed DJFOC is yielding a low false alarm rate compared to DSOC and DFOC.