Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

In recent times, sixth generation (6G) communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile users. It encompasses several heterogeneous resource and communication standard in ensuring incessant availability of service. At the same time, the development of 6G enables the Unmanned Aerial Vehicles (UAVs) in offering cost and time-efficient solution to several applications like healthcare, surveillance, disaster management, etc. In UAV networks, energy efficiency and data collection are considered the major process for high quality network communication. But these procedures are found to be challenging because of maximum mobility, unstable links, dynamic topology, and energy restricted UAVs. These issues are solved by the use of artificial intelligence (AI) and energy efficient clustering techniques for UAVs in the 6G environment. With this inspiration, this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles (AECCDC-UAV) in 6G environment. The proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head (CH) to each cluster in such a way that the energy consumption (ECM) gets minimized. The presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization (QOSSO) algorithm for selecting the CHs and construct clusters. The QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs, distance to neighboring UAVs, and degree of UAVs. The performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.