Reliability Modeling and Optimization Based on Master-Supporter Unmanned Aerial Vehicle Networks

In diverse operational scenarios of Unmanned Aerial Vehicle (UAV) systems, the master-supporter collaborative architecture has emerged as a prevalent networking paradigm for UAV teams. However, ensuring reliability in such networks under node failures remains a critical research problem. This study introduces an integrated reliability modeling and optimization framework for master-supporter UAV networks, where a leader UAV executes mission-critical operations while follower UAVs deliver real-time backup support and collaborative task execution. To resolve reliability challenges in Master-Supporter UAV networks, we propose a novel stochastic model integrating multi-state Markov processes with failure dependency. An adaptive redundancy allocation scheme is introduced to enable dynamic load balancing across drone clusters. Experimental validation demonstrates that the mastersupporter UAV architecture enhances system reliability compared to conventional static redundancy strategies in most operational scenarios. However, excessive workload pressure on supporter UAVs may negatively impact system reliability. This work provides systematic solutions for reliability assurance in 5G-integrated UAV swarms, precision agriculture monitoring, and emergency response systems.