用机器学习选关键特征，精准预测LoRaWAN设备的扩频因子。
Effective Feature Selection for Predicting Spreading Factor with ML in Large LoRaWAN-based Mobile IoT Networks
- 基于RSSI和SNR组合特征，提升扩频因子预测精度。
- 在31种特征组合中，仅用两个参数即达最优效果。
- 适合优化物联网设备续航与降低数据采集成本。
LoRaWAN是一种低功耗广域通信协议，本文研究如何利用机器学习预测其网络中的扩频因子（SF）。由于环境与网络条件波动，最优SF分配极具挑战。我们基于大规模公开数据集，评估了包括RSSI、SNR、频率、终端与网关距离、终端天线高度在内的多个核心特征，并实验了5个特征的31种组合。采用k-近邻（k-NN）、决策树（DTC）、随机森林（RF）和多项式逻辑回归（MLR）模型进行训练与验证。结果表明，仅使用RSSI与SNR组合即可达到最佳性能。该发现有助于减少模型训练所需的数据采集成本，延长终端设备电池寿命，推动更可靠的LoRaWAN系统建设。
原文摘要 · Abstract (English)
LoRaWAN is a low-power long-range protocol that enables reliable and robust communication. This paper addresses the challenge of predicting the spreading factor (SF) in LoRaWAN networks using machine learning (ML) techniques. Optimal SF allocation is crucial for optimizing data transmission in IoT-enabled mobile devices, yet it remains a challenging task due to the fluctuation in environment and network conditions. We evaluated ML model performance across a large publicly available dataset to explore the best feature across key LoRaWAN features such as RSSI, SNR, frequency, distance between end devices and gateways, and antenna height of the end device, further, we also experimented with 31 different combinations possible for 5 features. We trained and evaluated the model using k-nearest neighbors (k-NN), Decision Tree Classifier (DTC), Random Forest (RF), and Multinomial Logistic Regression (MLR) algorithms. The combination of RSSI and SNR was identified as the best feature set. The finding of this paper provides valuable information for reducing the overall cost of dataset collection for ML model training and extending the battery life of LoRaWAN devices. This work contributes to a more reliable LoRaWAN system by understanding the importance of specific feature sets for optimized SF allocation.
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