Adaptive Resource Optimization for LoRa-Enabled LEO Satellite IoT System in High-Dynamic Environments - Chen Zhang; Haoyou Peng; Yonghua Ji; Tao Hong; Gengxin Zhang (Sensors)
The integration of Low-Earth Orbit (LEO) satellites with Long Range Radio (LoRa)-based Internet of Things (IoT) systems for extensive wide-area coverage has gained traction in academia and industry, challenging traditional terrestrial resource optimization designed for semi-static single-base-station environments. This paper addresses LEO’s high dynamics and satellite-ground channel variability by introducing a beacon-triggered framework for LoRa-LEO IoT systems as a foundation for resource optimization. Then, in order to decouple the intertwined objectives of optimizing energy efficiency and maximizing the data extraction rate, an adaptive spreading factor (SF) allocation algorithm is proposed to mitigate collisions and resource waste, followed by a practical dynamic power control mechanism optimizing LoRa device power usage. Simulations validate that the proposed adaptive resource optimization outperforms conventional methods in dynamic, resource-constrained LEO environments, offering a robust solution for satellite IoT applications. In terms of energy efficiency and data extraction rate, the algorithm proposed in this paper outperforms other comparative algorithms. When the number of users reaches 3000, the energy efficiency is improved by at least 119%, and the data extraction rate is increased by at least 48%.