Abstract—Unmanned Aerial Vehicles (UAVs) have evolved into a potent form of data transmission, benefiting from the rapid advancements
in wireless communication technology. Furthermore, UAVs have demonstrated their effectiveness across diverse applications, such as
intelligent transportation, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly,
however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in
nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing
protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control
(EP-GARIC) cluster-based network topology. Moreover, the proposed model achieves the most efficient routing function through the utilization
of the novel Artificial Jellyfish Optimization (AJO) technique. The execution of this study is conducted within the Network Simulator (NS2)
environment. The outcomes of the simulations distinctly demonstrate the notable effectiveness of the suggested methodology. This is evidenced
by a marked decrease in energy consumption, a substantial improvement in packet delivery ratio, a noteworthy reduction in losses, and other
comparable metrics when contrasted with established conventional methods.
Keywords—Clustering, Neural Network, Fuzzy method, Energy Efficiency, Parameter Tuning.
