Metaheuristic Moth Flame Optimization Based Energy Efficient Clustering Protocol for 6G Enabled Unmanned Aerial Vehicle Networks

International audience Recently, 6G networks have gained significant attention among research communities due to their development in several real-time application areas. Unmanned aerial vehicles (UAVs) became popular because of the development of 6G networks. Besides, artificial intelligence (AI) techniques can be used for effective decision-making purposes in the 6G enabled UAV environment. This study introduces a metaheuristic moth flame optimization algorithm for energy efficient clustering (MMFO-EEC) technique for 6G enabled UAV networks. The major intention of the MMFO-EEC technique is the proficient election of cluster heads (CHs) and cluster organization in 6G enabled UAV networks. The presented MMFO-EEC technique mainly employs the MFO algorithm to effectually pick out the appropriate UAVs as CHs in the network. Besides, the MMFO-EEC technique derives a fitness function comprising distinct input parameters for accomplishing improved network performance. A wide range of simulations were carried out to highlight the enhancements of the MMFO-EEC technique, and the experimental values reported improved performance of the MMFO-EEC technique over the recent approaches.