A Secure Aware Routing Protocol for Efficient and Reliable FPANETS

The use of Unmanned Aerial Vehicles (UAVs) has evolved significantly, with a current focus on utilizing multiple UAVs in teams to achieve high-level objectives. To support this evolution, new networking frameworks are needed for the highly mobile nodes like UAVs in fleets. These frameworks enable both indirect and direct communication between nodes within communication range. However, constructing an ad hoc network between UAVs presents challenges, with criteria that differ from traditional networks such as Mobile Ad-hoc Networks (MANETs) and aerial ad-hoc networks using the Flower Pollination Algorithm Protocol (FPAP). This study presents network models of UAVs, highlights open research questions, explores opportunities and future directions, and discusses the challenges of deploying UAVs as ad hoc relay nodes. The research aims to enhance data transmission security in FPANETs to reduce the risks of black hole attacks. An Artificial Neural Network (ANN) model achieves 98.97% detection accuracy, while cluster-based routing streamlines network architecture and reduces complexity. Performance evaluations consider end-to-end latency, network throughput, hop count, and other network metrics. This paper highlights the effectiveness of these techniques in FPANET security and routing optimization.