Low Earth Orbit (LEO) satellites have been widely used to collect sensing data from ground-based IoT devices. Comprehensive and timely collection of sensor data is a prerequisite for conducting analysis, decision-making, and other tasks, ultimately enhancing services such as geological hazard monitoring and ecological environment monitoring. To improve the efficiency of data collection, many models and scheduling methods have been proposed, but they did not fully consider the practical scenarios of collecting data from remote areas with limited ground network coverage, particularly in addressing the uncertainties in data transmission caused by complex environments. To cope with the above challenges, this paper first presents a mathematical representation of the real-world scenario for data collection from geographically distributed IoT devices through LEO satellites, based on a full consideration of uncertainties in transmission rates. Then, a Cross-entropy-based transmission scheduling method (CETSM) and an uncertainty-aware transmission scheduling method (UATSM) are proposed to enhance the volume of collected data and mitigate the impact of uncertainty on the data uplink transmission rate. The CETSM achieved an average increase in total data collection ranging from 7.24% to 16.69% compared to the other five benchmark methods across eight scenarios. Moreover, UATSM performs excellently in the Monte Carlo-based evaluation module, achieving an average data collection completion rate of 96.1% and saving an average of 19.8% in energy costs, thereby obtaining a good balance between energy consumption and completion rate.

中文翻译：

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不确定性感知调度，通过 LEO 卫星从环境 IoT 设备有效收集数据


近地轨道 （LEO） 卫星已广泛用于从地面物联网设备收集传感数据。全面及时地采集传感器数据是进行分析、决策等任务的前提，最终提升地质灾害监测和生态环境监测等服务。为了提高数据采集的效率，人们提出了许多模型和调度方法，但它们没有充分考虑从地面网络覆盖有限的偏远地区采集数据的实际场景，特别是在解决复杂环境带来的数据传输不确定性方面。为了应对上述挑战，本文首先在充分考虑传输速率的不确定性的基础上，提出了通过 LEO 卫星从地理上分散的 IoT 设备收集数据的真实场景的数学表示。然后，提出了一种基于交叉熵的传输调度方法（CETSM）和一种不确定性感知传输调度方法（UATSM），以增强采集数据量，减轻不确定性对数据上行传输速率的影响。与其他五种基准方法相比，CETSM 在 8 种情景中实现了 7.24% 至 16.69% 的总数据收集平均增长。此外，UATSM 在基于蒙特卡洛的评估模块中表现优异，平均数据收集完成率为 96.1%，平均节省 19.8% 的能源成本，从而在能耗和完成率之间取得良好的平衡。