Robust Peer-to-Peer Selection in FANETs With Uncertain Link Quality: A SAO-Based Approach for Improving Throughput, Fairness, and Energy Efficiency

Flying Ad Hoc Networks (FANETs) face significant challenges in maintaining reliable peer-to-peer (P2P) communication due to high node mobility, dynamic network topologies, and uncertain link quality, which collectively degrade throughput, energy efficiency, and fairness. To address this problem, this paper proposes a Smell Agent Optimization-based Peer-to-Peer Selection (SAO-P2PS) algorithm designed to optimize peer selection under realistic channel uncertainties. The methodology uses a bio-inspired SAO framework that dynamically adapts to fluctuating link conditions by modeling peer selection as a multi-objective optimization problem, simultaneously maximizing throughput and energy efficiency while ensuring equitable resource distribution using fairness index. The algorithm incorporates uncertain link quality estimates directly into its utility function, enabling robust decision-making in highly dynamic environments. Simulation results show that SAO-P2PS significantly outperforms conventional Random Selection and Nearest-Neighbor Selection methods, achieving 25-35% higher throughput, 40-60% better energy efficiency, and maintaining fairness indices above 0.6 across varying link quality conditions. The proposed algorithm exhibits rapid convergence within 20-30 iterations and maintains stable performance despite UAV mobility and channel fluctuations. The significance of these findings lies in providing a computationally efficient, adaptive solution for real-time FANET applications where reliable P2P communication is critical for mission success. This research establishes SAO-P2PS as a foundation for intelligent peer selection strategies in next-generation aerial networks, with potential applications in disaster response, surveillance, and autonomous swarm operations in 5G-and-beyond communication ecosystems.