面向无人机数据采集的LoRa扩频因子预测模型研究

Research on the LoRa spreading factor prediction model for UAV data collection

针对在缺少移动网络覆盖的偏远地区实现大面积数据采集与环境监测,首先设计了无人机移动网关与地面节点的LoRa通信协议;在此基础上提出了一种基于改进极限学习机(PG-ELM)的扩频因子预测模型,以实现扩频因子的动态调整。为提高预测准确度与效率,该模型以信号强度、信噪比、距离、丢包率、温度和相对湿度作为输入,以粒子群算法(PSO)和灰狼算法(GWO)联合算法对ELM模型进行改进。通过无人机移动通信试验获取LoRa通信数据样本集,进行模型训练获得优化的PG-ELM模型。试验结果表明,在20 kB数据大小的情况下,本方案的数据采集时间比单一SF12、SF7减少约78%和26%,平均通信能耗比单一SF12降低70%以上,数据包投递率(PDR)高达98%,在能效性和预测实时性等方面优势明显。

For large area data collection and environmental monitoring in remote areas with no mobile network coverage,this article first designs a LoRa communication protocol between the UAV mobile gateway and the ground nodes.Based on this,a spreading factor prediction model based on the improved extreme learning machine(PG-ELM)is proposed to achieve dynamic optimization and adjustment of the spreading factor.To improve the prediction accuracy and efficiency,the model uses signal strength,signal-to-noise ratio,distance,packet loss rate,temperature and relative humidity as inputs.The particle swarm optimization algorithm and the grey wolf optimization algorithm are fused to optimize the ELM model.The LoRa communication data sample sets are obtained through the UAV mobile communication experiment,which are then used to train and optimize the PG-ELM model.The results show that,with a data size of 20 kB,the proposed scheme reduces the data collection time by about 78%and 26%compared with single SF12 and SF7.It also lowers the average communication energy consumption by more than 70%compared with single SF12,achieves a packet delivery rate of 98%,and has significant advantages in energy efficiency and prediction real-time performance.