Optimizing Data Transmission in Large-Scale Sensor Networks Using UAVs: A Segmentation-Clustering and Sequential Approach

In outdoor environments, the demand for real-time data transmission from large-scale sensor networks necessitates innovative solutions. This study introduces a system architecture aimed at optimizing real-time data transmission performance using fixed-wing unmanned aerial vehicles (UAVs). By deriving energy consumption and network throughput models for UAV flight, we investigate the relationships between key parameters such as sensor node (SN) distribution, UAV flight center position, and flight radius with throughput and UAV flight energy consumption. Subsequently, we establish an optimization model for UAV flight parameters to enhance both data transmission efficiency of SNs and energy efficiency of UAVs. To address the computational challenges posed by large-scale sensor network applications, we propose the segmentation-iteration clustering (SEG-C) algorithm to reduce computational complexity. Additionally, we develop the sequential optimization algorithm (SOA) by decomposing and reconstructing the complex nonconvex multiobjective optimization model and its constraints. Simulation results demonstrate that the SOA achieves optimal UAV flight parameters while maintaining the throughput threshold. Furthermore, the SEG-C algorithm significantly reduces computational scale and enhances solution efficiency, particularly in managing large-scale sensor applications.