Balancing Time and Energy Efficiency by Sizing Clusters: A New Data Collection Scheme in UAV-Aided Large-Scale Internet of Things.

Unmanned Aerial Vehicle (UAV)-aided large-scale Internet of Things (UAV-LIoT) are widely used but lack a balanced data collection scheme. To address this, we propose DC-NOMA, a new data collection (DC) scheme that combines machine learning clustering with Non-Orthogonal Multiple Access (NOMA). We introduce an optimization algorithm for peak density clustering and a new LIoT clustering method. Our approach dynamically adjusts cluster size and formulates the energy-time efficiency problem as a trade-off between energy minimization and data rate maximization. We propose a heuristic algorithm based on NOMA and an intra-cluster data collection protocol. Experimental results show that DC-NOMA achieves balanced data collection time, energy efficiency, load balance, and network lifespan extension, outperforming its benchmarks.