ABSTRACT
The need for wireless networks among users and their unique features make FANETs an attractive and emerging technology. FANET-based research and development, both in academia and business, has surged in recent years. Due to their unique qualities for many vital mission applications, unmanned aerial vehicles (UAVs) are being used more and more for a range of missions, such as traffic surveillance, video graphics, and military, and civilian operations. The research proposes a back propagation neural network technique based on supervised learning for clustering-based location-aware and energy-efficient routing. The suggested results show that, for FANET, the network lifetime effectively rises and the energy consumption is somewhat decreased. A developing method for estimating network performance and achieving an energy-efficient solution which is achieved by utilizing the suggested approach is supervised learning.
