Adaptive ant colony methods for UAV LEO coordination in non terrestrial IoT

Introduction This work presents an adaptive ant colony (AdCO) framework for dynamic task management in heterogeneous Non-Terrestrial Network–Internet of Things (NTN-IoT) systems integrating Unmanned Aerial Vehicles (UAVs) and Low Earth Orbit (LEO) satellites. The framework addresses key challenges such as stochastic mobility, intermittent connectivity, and latency-sensitive operations common in large-scale IoT deployments. Methods The proposed approach employs adaptive pheromone learning, heuristic control, and multi-timescale scheduling. It follows a hierarchical co-optimization strategy, where UAV swarms perform edge-side task allocation while LEO satellites handle relay scheduling during orbital passes. Event-triggered pheromone resets and distributionally robust cost modeling are introduced to maintain stability and adaptability under dynamic network conditions. Results Simulation results demonstrate superior performance compared to classical Ant Colony Optimization (ACO) and recent meta-heuristic methods. The proposed model achieves higher task completion ratios, reduced end-to-end latency, and enhanced energy-normalized throughput across different orbital configurations, traffic patterns, and link failures. Discussion The findings confirm the efficiency and resilience of the proposed framework in NTN-IoT operations. Its adaptability makes it suitable for critical applications such as disaster response, precision agriculture, and maritime monitoring, where real-time coordination and reliability are essential.