Optimal selection based K-mean clustering technique to improve the energy efficiency in cognitive radio networks for 6G applications

The main objective of the cognitive radio sensor network (CRSN) is to minimize the energy consumption in channel sensing and switching when sensor nodes sense and switch to a licensed channel for improving the energy efficiency for 6G applications. The drawback in CRSN is that the sensor network uses a higher amount of energy by intrabunch and interbunch of data emission energetic medium ingression assignment problem when the sensor nodes sense the channel. In this brief, a K-mean clustering technique (KMCT) is proposed for CRSN to minimize the energy used by the sensor nodes in CRSN for 6G applications. The KMCT-based joint power medium ingression technique (JPMIT) is introduced for enhancing the energy efficiency in a bunch of interdata emissions, and the KMCT-based energetic medium ingression technique (EMIT) is introduced for minimizing the energy utilization in a bunch of intradata emissions. Simulation results show that the proposed KMCT for CRSN provides a better result compared with existing techniques. The performance parameters such as medium available duration, data loss rate, quantity of data, number of authorized mediums, and probability of false alarm have been analyzed using MATLAB R2012a.