Abstract: - Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of
evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including
intelligent transport, 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. Furthermore, the optimal routing function is achieved by the proposed Artificial
Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results
displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio,
reduced loss, and so on over compared to the conventional approaches.
Keywords: Clustering, Neural Network, Fuzzy method, Energy Efficiency, Parameter Tuning.
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