【摘要】 针对现有的LoRaWAN自适应数据速率控制算法在网络拥塞情况下会导致数据传输效率较低的问题,提出一种将逻辑回归分类器和调整退避时间的改进型自适应数据速率控制器相结合的方法。分类器通过逻辑回归学习得到预测的拥塞状态;数据速率控制器根据预测的拥塞状态确定数据速率调整方案。实验结果表明,该方法比原有的LoRaWAN自适应数据速率控制算法更高效。更多还原

【Abstract】 The existing LoRaWAN adaptive data rate control algorithm leads to low data transmission efficiency in the case of network congestion. To solve this problem, this paper proposed an improved adaptive data rate controller which combined logistic regression classifier with adjusting back-off time. The classifier obtained the predicted congestion state by logistic regression learning. The data rate controller determined the data rate adjustment scheme according to the predicted congestion state. The experimental results show that this method is more efficient than the original LoRaWAN adaptive data rate control algorithm.更多还原